Automated eligibility screening and monitoring for genotype-driven precision oncology trials

OncoCTMiner: streamlining precision oncology trial matching via molecular profile analysis

By establishing omics sequencing of patient tumors as a crucial element in cancer treatment, the extensive implementation of precision oncology necessitates effective and prompt execution of clinical studies for approving molecular-targeted therapies. However, the substantial volume of patient sequencing data, combined with strict clinical trial criteria, increasingly complicates the process of matching patients to precision oncology studies. To streamline enrollment in these studies, we developed OncoCTMiner, an automated pre-screening platform for molecular cancer clinical trials. Through manual tagging of eligibility criteria for 2227 oncology trials, we identified key bio-concepts such as cancer types, genes, alterations, drugs, biomarkers and therapies. Utilizing this manually annotated corpus along with open-source biomedical natural language processing tools, we trained multiple named entity recognition models specifically designed for precision oncology trials. These models analyzed 460 952 clinical trials, revealing 8.15 million precision medicine concepts, 9.32 million entity-criteria-trial triplets and a comprehensive precision oncology eligibility criteria database. Most significantly, we developed a patient-trial matching system based on cancer patients' clinical and genetic profiles, which can seamlessly integrate with the omics data analysis platform. This system expedites the pre-screening process for potentially suitable precision oncology trials, offering patients swifter access to promising treatment options. Database URL  https://oncoctminer.chosenmedinfo.com.

Progressive plasticity during colorectal cancer metastasis

Metastasis is the principal cause of cancer death, yet we lack an understanding of metastatic cell states, their relationship to primary tumor states, and the mechanisms by which they transition. In a cohort of biospecimen trios from same-patient normal colon, primary and metastatic colorectal cancer, we show that while primary tumors largely adopt LGR5 + intestinal stem-like states, metastases display progressive plasticity. Loss of intestinal cell states is accompanied by reprogramming into a highly conserved fetal progenitor state, followed by non-canonical differentiation into divergent squamous and neuroendocrine-like states, which is exacerbated by chemotherapy and associated with poor patient survival. Using matched patient-derived organoids, we demonstrate that metastatic cancer cells exhibit greater cell-autonomous multilineage differentiation potential in response to microenvironment cues than their intestinal lineage-restricted primary tumor counterparts. We identify PROX1 as a stabilizer of intestinal lineage in the fetal progenitor state, whose downregulation licenses non-canonical reprogramming.

Organizing precision medicine: A case study of Memorial Sloan Kettering Cancer Center's engagement in/with genomics

Recent decades have seen a dramatic rise of in the number of initiatives designed to promote precision oncology, a domain that has played a pioneering role in the implementation of post-genomic approaches and technologies such as innovative clinical trial designs and molecular profiling. In this paper, based on fieldwork carried out at the Memorial Sloan-Kettering Cancer Center from 2019 onwards, we analyze how a world-leading cancer center has adapted, responded, and contributed to the challenge of "doing" precision oncology by developing new programs and services, and building an infrastructure that has created the conditions for genomic practices. We do so by attending to the "organizing" side of precision oncology and to the nexus between these activities and epistemic issues. We situate the work that goes into making results actionable and accessing targeted drugs within the larger process of creating a precision medicine ecosystem that includes purpose-built institutional settings, thus simultaneously experimenting with bioclinical matters and, reflexively, with organizing practices. The constitution and articulation of innovative sociotechnical arrangements at MSK provides a unique case study of the production of a large and complex clinical research ecosystem designed to implement rapidly evolving therapeutic strategies embedded in a renewed and dynamic understanding of cancer biology.

Molecular Biomarkers of Disease Outcomes and Mechanisms of Acquired Resistance to First-Line Osimertinib in Advanced EGFR-Mutant Lung Cancers

INTRODUCTION

Preferred first-line treatment for patients with metastatic EGFR-mutant lung cancer is osimertinib, yet it is not known whether patient outcomes may be improved by identifying and intervening on molecular markers associated with therapeutic resistance.

METHODS

All patients with metastatic EGFR-mutant lung cancer treated with first-line osimertinib at the Memorial Sloan Kettering Cancer Center (n = 327) were identified. Available pretreatment and postprogression tumor samples underwent targeted gene panel sequencing and mutational signature analysis using SigMA algorithm. Progression-free survival (PFS) and overall survival were estimated using the Kaplan-Meier method.

RESULTS

Using multivariate analysis, baseline atypical EGFR (median PFS = 5.8 mo, p < 0.001) and concurrent TP53/RB1 alterations (median PFS = 10.5 mo, p = 0.015) were associated with shorter PFS on first-line osimertinib. Of 95 patients with postprogression biopsies, acquired resistance mechanisms were identified in 52% (off-target, n = 24; histologic transformation, n = 14; on-target, n = 12), with MET amplification (n = 9), small cell lung transformation (n = 7), and acquired EGFR amplification (n = 7), the most frequently identified mechanisms. Although there was no difference in postprogression survival on the basis of identified resistance (p = 0.07), patients with subsequent second-line therapy tailored to postprogression biopsy results had improved postprogression survival (hazard ratio = 0.09, p = 0.006). The paired postprogression tumors had higher tumor mutational burden (p = 0.008) and further dominant APOBEC mutational signatures (p = 0.07) compared with the pretreatment samples.

CONCLUSIONS

Patients with EGFR-mutant lung cancer treated with first-line osimertinib have improved survival with treatment adaptation on the basis of identified mechanisms of resistance at time of progression using tissue-based genomic analysis. Further survival gains may be achieved using risk-based treatment adaptation of pretreatment genomic alterations.

Implementation and Clinical Adoption of Precision Oncology Workflows Across a Healthcare Network

BACKGROUND

Precision oncology relies on molecular diagnostics, and the value-proposition of modern healthcare networks promises a higher standard of care across partner sites. We present the results of a clinical pilot to standardize precision oncology workflows.

METHODS

Workflows are defined as the development, roll-out, and updating of disease-specific molecular order sets. We tracked the timeline, composition, and effort of consensus meetings to define the combination of molecular tests. To assess clinical impact, we examined order set adoption over a two-year period (before and after roll-out) across all gastrointestinal and hepatopancreatobiliary (GI) malignancies, and by provider location within the network.

RESULTS

Development of 12 disease center-specific order sets took ~9 months, and the average number of tests per indication changed from 2.9 to 2.8 (P = .74). After roll-out, we identified significant increases in requests for GI patients (17%; P < .001), compliance with testing recommendations (9%; P < .001), and the fraction of "abnormal" results (6%; P < .001). Of 1088 GI patients, only 3 received targeted agents based on findings derived from non-recommended orders (1 before and 2 after roll-out); indicating that our practice did not negatively affect patient treatments. Preliminary analysis showed 99% compliance by providers in network sites, confirming the adoption of the order sets across the network.

CONCLUSION

Our study details the effort of establishing precision oncology workflows, the adoption pattern, and the absence of harm from the reduction of non-recommended orders. Establishing a modifiable communication tool for molecular testing is an essential component to optimize patient care via precision oncology.

MatchMiner: an open-source platform for cancer precision medicine

Widespread, comprehensive sequencing of patient tumors has facilitated the usage of precision medicine (PM) drugs to target specific genomic alterations. Therapeutic clinical trials are necessary to test new PM drugs to advance precision medicine, however, the abundance of patient sequencing data coupled with complex clinical trial eligibility has made it challenging to match patients to PM trials. To facilitate enrollment onto PM trials, we developed MatchMiner, an open-source platform to computationally match genomically profiled cancer patients to PM trials. Here, we describe MatchMiner’s capabilities, outline its deployment at Dana-Farber Cancer Institute (DFCI), and characterize its impact on PM trial enrollment. MatchMiner’s primary goals are to facilitate PM trial options for all patients and accelerate trial enrollment onto PM trials. MatchMiner can help clinicians find trial options for an individual patient or provide trial teams with candidate patients matching their trial’s eligibility criteria. From March 2016 through March 2021, we curated 354 PM trials containing a broad range of genomic and clinical eligibility criteria and MatchMiner facilitated 166 trial consents (MatchMiner consents, MMC) for 159 patients. To quantify MatchMiner’s impact on trial consent, we measured time from genomic sequencing report date to trial consent date for the 166 MMC compared to trial consents not facilitated by MatchMiner (non-MMC). We found MMC consented to trials 55 days (22%) earlier than non-MMC. MatchMiner has enabled our clinicians to match patients to PM trials and accelerated the trial enrollment process.

Challenges and Obstacles in Applying Therapeutical Indications Formulated in Molecular Tumor Boards

Considering the rapid improvement of cancer drugs' efficacy and the discovery of new molecular targets, the formulation of therapeutical indications based on the multidisciplinary approach of MTB is becoming increasingly important for attributing the correct salience to the targets identified in a single patient. Nevertheless, one of the biggest stumbling blocks faced by MTBs is not the bare indication, but its implementation in the clinical practice. Indeed, administering the drug suggested by MTB deals with some relevant difficulties: the economical affordability and geographical accessibility represent some of the major limits in the patient's view, while bureaucracy and regulatory procedures are often a disincentive for the physicians. In this review, we explore the current literature reporting MTB experiences and precision medicine clinical trials, focusing on the challenges that authors face in applying their therapeutical indications. Furthermore, we analyze and discuss some of the solutions devised to overcome these difficulties to support the MTBs in finding the most suitable solution for their specific situation. In conclusion, we strongly encourage regulatory agencies and pharmaceutical companies to develop effective strategies with medical centers implementing MTBs to facilitate access to innovative drugs and thereby allow broader therapeutical opportunities to patients.

Immunogenicity and therapeutic targeting of a public neoantigen derived from mutated PIK3CA

Public neoantigens (NeoAgs) represent an elite class of shared cancer-specific epitopes derived from recurrently mutated driver genes. Here we describe a high-throughput platform combining single-cell transcriptomic and T cell receptor (TCR) sequencing to establish whether mutant PIK3CA, among the most frequently genomically altered driver oncogenes, generates an immunogenic public NeoAg. Using this strategy, we developed a panel of TCRs that recognize an endogenously processed neopeptide encompassing a common PIK3CA hotspot mutation restricted by the prevalent human leukocyte antigen (HLA)-A*03:01 allele. Mechanistically, immunogenicity to this public NeoAg arises from enhanced neopeptide/HLA complex stability caused by a preferred HLA anchor substitution. Structural studies indicated that the HLA-bound neopeptide presents a comparatively 'featureless' surface dominated by the peptide's backbone. To bind this epitope with high specificity and affinity, we discovered that a lead TCR clinical candidate engages the neopeptide through an extended interface facilitated by an unusually long CDR3β loop. In patients with diverse malignancies, we observed NeoAg clonal conservation and spontaneous immunogenicity to the neoepitope. Finally, adoptive transfer of TCR-engineered T cells led to tumor regression in vivo in mice bearing PIK3CA-mutant tumors but not wild-type PIK3CA tumors. Together, these findings establish the immunogenicity and therapeutic potential of a mutant PIK3CA-derived public NeoAg.

Prescreening to Increase Therapeutic Oncology Trial Enrollment at the Largest Public Hospital in the United States

PURPOSE

The recruitment of underserved patients into therapeutic oncology trials is imperative. The National Institutes of Health mandates the inclusion of minorities in clinical research, although their participation remains under-represented. Institutions have used data mining to match patients to clinical trials. In a public health care system, such expensive tools are unavailable.

METHODS

The NYU Clinical Trials Office implemented a quality improvement program at Bellevue Hospital Cancer Center to increase therapeutic trial enrollment. Patients are screened through the electronic medical record, tumor board conferences, and the cancer registry. Our analysis evaluated two variables: number of patients identified and those enrolled into clinical trials.

RESULTS

Two years before the program, there were 31 patients enrolled. For a period of 24 months (July 2017 to July 2019), we identified 255 patients, of whom 143 (56.1%) were enrolled. Of those enrolled, 121 (84.6%) received treatment, and 22 (15%) were screen failures. Fifty-five (38.5%) were referred to NYU Perlmutter Cancer Center for therapy. Of the total enrollees, 64% were female, 56% were non-White, and overall median age was 55 years (range: 33-88 years). Our participants spoke 16 different languages, and 57% were non-English-speaking. We enrolled patients into eight different disease categories, with 38% recruited to breast cancer trials. Eighty-three percent of our patients reside in low-income areas, with 62% in both low-income and Health Professional Shortage Areas.

CONCLUSION

Prescreening at Bellevue has led to a 4.6-fold increase in patient enrollment to clinical trials. Future research into using prescreening programs at public institutions may improve access to clinical trials for underserved populations.

Regulations, Open Data and Healthcare Innovation: A Case of MSK-IMPACT and Its Implications for Better Cancer Care

Simple Summary

The advancement in both science and technology has contributed to the development of novel diagnostic technologies; such technologies enable medical practitioners to diagnose diseases that could not be previously detected. However, in order to translate new technologies into practical applications, various types of challenges need to be overcome. To address these challenges, including those in clinical management and regulatory science, healthcare policies have been constantly implemented to promote the practical application of outcomes generated by healthcare innovation. This study conducted comparative analyses of three tumor profiling tests approved by the U.S. Food and Drug Administration (FDA) in 2017, hypothesizing that the FDA’s regulatory reforms, early application of new technologies to both research and clinical settings, and open data accumulated as a result of large-scale research programs have promoted new drug development in oncology. The study then discussed the implications potentially suggested by the outcomes and challenges of the three tests.

Abstract

This study investigated a case of Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT), a tumor profiling test approved by the U.S. Food and Drug Administration (FDA) in 2017, to examine what factors would contribute to healthcare innovation. First, we set the following three parameters to observe cases: (i) the FDA regulatory reforms, (ii) early application of new technologies, such as next-generation sequencing (NGS), to both research and clinical settings, and (iii) accumulation of open data. Then, we performed a comparative analysis of MSK-IMPACT with FoundationOne CDx and Oncomine Dx Target Test, both of which were FDA-approved tumor profiling tests launched in 2017. As a result, we found that MSK-IMPACT secures neutrality as a non-profit organization, achieves the active incorporation of basic research results, and performs superiorly in clinical operations, such as patient enrollment. On the contrary, we confirmed that FoundationOne CDx was the most prominent case in terms of the number of new drugs and expanded indications approved in which the FDA’s expedited approval programs were considerably utilized. Consequently, to uncover the full potential of MSK-IMPACT, it is suggested that more intersectoral collaborative activities between various healthcare stakeholders, in particular, pharmaceutical companies, for driving clinical development must be carried out based on an organizational framework that facilitates collaboration.

The Future of Clinical Trial Design in Oncology

Clinical trials represent a fulcrum for oncology drug discovery and development to bring safe and effective medicines to patients in a timely manner. Clinical trials have shifted from traditional studies evaluating cytotoxic chemotherapy in largely histology-based populations to become adaptively designed and biomarker-driven evaluations of molecularly targeted agents and immune therapies in selected patient subsets. This review will discuss the scientific, methodological, practical, and patient-focused considerations to transform clinical trials. A call to action is proposed to establish the framework for next-generation clinical trials that strikes an optimal balance of operational efficiency, scientific impact, and value to patients. SIGNIFICANCE: The future of cancer clinical trials requires a framework that can efficiently transform scientific discoveries to clinical utility through applications of innovative technologies and dynamic design methodologies. Next-generation clinical trials will offer individualized strategies which ultimately contribute to globalized knowledge and collective learning, through the joint efforts of all key stakeholders including investigators and patients.

Evaluation of an artificial intelligence clinical trial matching system in Australian lung cancer patients

Objective

The objective of this technical study was to evaluate the performance of an artificial intelligence (AI)-based system for clinical trials matching for a cohort of lung cancer patients in an Australian cancer hospital.

Methods

A lung cancer cohort was derived from clinical data from patients attending an Australian cancer hospital. Ten phases I–III clinical trials registered on clinicaltrials.gov and open to lung cancer patients at this institution were utilized for assessments. The trial matching system performance was compared to a gold standard established by clinician consensus for trial eligibility.

Results

The study included 102 lung cancer patients. The trial matching system evaluated 7252 patient attributes (per patient median 74, range 53–100) against 11 467 individual trial eligibility criteria (per trial median 597, range 243–4132). Median time for the system to run a query and return results was 15.5 s (range 7.2–37.8). In establishing the gold standard, clinician interrater agreement was high (Cohen’s kappa 0.70–1.00). On a per-patient basis, the performance of the trial matching system for eligibility was as follows: accuracy, 91.6%; recall (sensitivity), 83.3%; precision (positive predictive value), 76.5%; negative predictive value, 95.7%; and specificity, 93.8%.

Discussion and Conclusion

The AI-based clinical trial matching system allows efficient and reliable screening of cancer patients for clinical trials with 95.7% accuracy for exclusion and 91.6% accuracy for overall eligibility assessment; however, clinician input and oversight are still required. The automated system demonstrates promise as a clinical decision support tool to prescreen a large patient cohort to identify subjects suitable for further assessment.

Genomic expertise in action: molecular tumour boards and decision-making in precision oncology

The recent development of cancer precision medicine is associated with the emergence of 'molecular tumour boards' (MTBs). Attended by a heterogenous set of practitioners, MTBs link genomic platforms to clinical practices by establishing 'actionable' connections between drugs and molecular alterations. Their activities rely on a number of evidential resources - for example databases, clinical trial results, basic knowledge about mutations and pathways - that need to be associated with the clinical trajectory of individual patients. Experts from various domains are required to master and align diverse kinds of information. However, rather than examining MTBs as an institution interfacing different kinds of expertise embedded in individual experts, we argue that expertise is the emergent outcome of MTBs, which can be conceptualised as networks or 'agencements' of humans and devices. Based on the ethnographic analysis of the activities of four clinical trial MTBs (three in France and an international one) and of two French routine-care MTBs, the paper analyses how MTBs produce therapeutic decisions, centring on the new kind of expertise they engender. The development and activities of MTBs signal a profound transformation of the evidentiary basis and processes upon which biomedical expertise and decision-making in oncology are predicated and, in particular, the emergence of a clinic of variants.

A Real-Time Automated Patient Screening System for Clinical Trials Eligibility in an Emergency Department: Design and Evaluation

BACKGROUND

One critical hurdle for clinical trial recruitment is the lack of an efficient method for identifying subjects who meet the eligibility criteria. Given the large volume of data documented in electronic health records (EHRs), it is labor-intensive for the staff to screen relevant information, particularly within the time frame needed. To facilitate subject identification, we developed a natural language processing (NLP) and machine learning-based system, Automated Clinical Trial Eligibility Screener (ACTES), which analyzes structured data and unstructured narratives automatically to determine patients' suitability for clinical trial enrollment. In this study, we integrated the ACTES into clinical practice to support real-time patient screening.

OBJECTIVE

This study aimed to evaluate ACTES's impact on the institutional workflow, prospectively and comprehensively. We hypothesized that compared with the manual screening process, using EHR-based automated screening would improve efficiency of patient identification, streamline patient recruitment workflow, and increase enrollment in clinical trials.

METHODS

The ACTES was fully integrated into the clinical research coordinators' (CRC) workflow in the pediatric emergency department (ED) at Cincinnati Children's Hospital Medical Center. The system continuously analyzed EHR information for current ED patients and recommended potential candidates for clinical trials. Relevant patient eligibility information was presented in real time on a dashboard available to CRCs to facilitate their recruitment. To assess the system's effectiveness, we performed a multidimensional, prospective evaluation for a 12-month period, including a time-and-motion study, quantitative assessments of enrollment, and postevaluation usability surveys collected from the CRCs.

RESULTS

Compared with manual screening, the use of ACTES reduced the patient screening time by 34% (P<.001). The saved time was redirected to other activities such as study-related administrative tasks (P=.03) and work-related conversations (P=.006) that streamlined teamwork among the CRCs. The quantitative assessments showed that automated screening improved the numbers of subjects screened, approached, and enrolled by 14.7%, 11.1%, and 11.1%, respectively, suggesting the potential of ACTES in streamlining recruitment workflow. Finally, the ACTES achieved a system usability scale of 80.0 in the postevaluation surveys, suggesting that it was a good computerized solution.

CONCLUSIONS

By leveraging NLP and machine learning technologies, the ACTES demonstrated good capacity for improving efficiency of patient identification. The quantitative assessments demonstrated the potential of ACTES in streamlining recruitment workflow and improving patient enrollment. The postevaluation surveys suggested that the system was a good computerized solution with satisfactory usability.

Real-World Outcomes of an Automated Physician Support System for Genome-Driven Oncology

PURPOSE

Matching patients to investigational therapies requires new tools to support physician decision making. We designed and implemented Precision Insight Support Engine (PRECISE), an automated, just-in-time, clinical-grade informatics platform to identify and dynamically track patients on the basis of molecular and clinical criteria. Real-world use of this tool was analyzed to determine whether PRECISE facilitated enrollment to early-phase, genome-driven trials.

MATERIALS AND METHODS

We analyzed patients who were enrolled in genome-driven, early-phase trials using PRECISE at Memorial Sloan Kettering Cancer Center between April 2014 and January 2018. Primary end point was the proportion of enrolled patients who were successfully identified using PRECISE before enrollment. Secondary end points included time from sequencing and PRECISE identification to enrollment. Reasons for a failure to identify genomically matched patients were also explored.

RESULTS

Data were analyzed from 41 therapeutic trials led by 19 principal investigators. In total, 755 patients were accrued to these studies during the period that PRECISE was used. PRECISE successfully identified 327 patients (43%) before enrollment. Patients were diagnosed with 29 tumor types and harbored alterations in 43 oncogenes, most commonly ERBB2 (21.3%), PIK3CA (14.1%), and BRAF (8.7%). Median time from sequencing to enrollment was 163 days (interquartile range, 66 to 357 days), and from PRECISE identification to enrollment 87 days (interquartile range, 37 to 180 days). Common reasons for failing to identify patients before enrollment included accrual on the basis of molecular alterations that did not match pre-established PRECISE genomic eligibility (140 [33%] of 428) and external sequencing not available for parsing (127 [30%] of 428).

CONCLUSION

PRECISE identified 43% of all patients accrued to a diverse cohort of early-phase, genome-matched studies. Purpose-built informatics platforms represent a novel and potentially effective method for matching patients to molecularly selected studies.

A Curated Cancer Clinical Outcomes Database (C3OD) for accelerating patient recruitment in cancer clinical trials

Data used to determine patient eligibility for cancer clinical trials often come from disparate sources that are typically maintained by different groups within an institution, use differing technologies, and are stored in different formats. Collecting data and resolving inconsistencies across sources increase the time it takes to screen eligible patients, potentially delaying study completion. To address these challenges, the Biostatistics and Informatics Shared Resource at The University of Kansas Cancer Center developed the Curated Cancer Clinical Outcomes Database (C3OD). C3OD merges data from the electronic medical record, tumor registry, bio-specimen and data registry, and allows querying through a single unified platform. By centralizing access and maintaining appropriate controls, C3OD allows researchers to more rapidly obtain detailed information about each patient in order to accelerate eligibility screening. This case report describes the design of this informatics platform as well as initial assessments of its reliability and usability.

Basket Studies: Redefining Clinical Trials in the Era of Genome-Driven Oncology

Understanding a tumor's detailed molecular profile has become increasingly necessary to deliver the standard of care for patients with advanced cancer. Innovations in both tumor genomic sequencing technology and the development of drugs that target molecular alterations have fueled recent gains in genome-driven oncology care. "Basket studies," or histology-agnostic clinical trials in genomically selected patients, represent one important research tool to continue making progress in this field. We review key aspects of genome-driven oncology care, including the purpose and utility of basket studies, biostatistical considerations in trial design, genomic knowledgebase development, and patient matching and enrollment models, which are critical for translating our genomic knowledge into clinically meaningful outcomes.

Implementing Genome-Driven Oncology

Early successes in identifying and targeting individual oncogenic drivers, together with the increasing feasibility of sequencing tumor genomes, have brought forth the promise of genome-driven oncology care. As we expand the breadth and depth of genomic analyses, the biological and clinical complexity of its implementation will be unparalleled. Challenges include target credentialing and validation, implementing drug combinations, clinical trial designs, targeting tumor heterogeneity, and deploying technologies beyond DNA sequencing, among others. We review how contemporary approaches are tackling these challenges and will ultimately serve as an engine for biological discovery and increase our insight into cancer and its treatment.

Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically defined tumor types, coupled with an expanding portfolio of molecularly targeted therapies, demands flexible and comprehensive approaches to profile clinically relevant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative using a comprehensive assay, MSK-IMPACT, through which we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel noncoding alterations, and mutational signatures that were shared by common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically defined tumor types, coupled with an expanding portfolio of molecularly targeted therapies, demands flexible and comprehensive approaches to profile clinically relevant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative using a comprehensive assay, MSK-IMPACT, through which we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel noncoding alterations, and mutational signatures that were shared by common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.