DNA Mutational and Copy Number Variation Profiling of Primary Craniofacial Osteosarcomas by Next-Generation Sequencing.

BACKGROUND: Craniofacial osteosarcomas (CFOS) are uncommon malignant neoplasms of the head and neck with different clinical presentation, biological behavior and prognosis from conventional osteosarcomas of long bones. Very limited genetic data have been published on CFOS. METHODS: In the current study, we performed comprehensive genomic studies in 15 cases of high-grade CFOS by SNP array and targeted next generation sequencing. RESULT: Our study shows high-grade CFOS demonstrate highly complex and heterogenous genomic alterations and harbor frequently mutated tumor suppressor genes TP53, CDKN2A/B, and PTEN, similar to conventional osteosarcomas. Potentially actionable gene amplifications involving CCNE1, AKT2, MET, NTRK1, PDGFRA, KDR, KIT, MAP3K14, FGFR1, and AURKA were seen in 43% of cases. GNAS hotspot activating mutations were also identified in a subset of CFOS cases, with one case representing malignant transformation from fibrous dysplasia, suggesting a role for GNAS mutation in the development of CFOS. CONCLUSION: High-grade CFOS demonstrate highly complex and heterogenous genomic alterations, with amplification involving receptor tyrosine kinase genes, and frequent mutations involving tumor suppressor genes.

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Quantifying the Expanding Landscape of Clinical Actionability for Patients with Cancer.

There is a continuing debate about the proportion of cancer patients that benefit from precision oncology, attributable in part to conflicting views as to which molecular alterations are clinically actionable. To quantify the expansion of clinical actionability since 2017, we annotated 47,271 solid tumors sequenced with the MSK-IMPACT clinical assay using two temporally distinct versions of the OncoKB knowledge base deployed 5 years apart. Between 2017 and 2022, we observed an increase from 8.9% to 31.6% in the fraction of tumors harboring a standard care (level 1 or 2) predictive biomarker of therapy response and an almost halving of tumors carrying nonactionable drivers (44.2% to 22.8%). In tumors with limited or no clinical actionability, TP53 (43.2%), KRAS (19.2%), and CDKN2A (12.2%) were the most frequently altered genes. SIGNIFICANCE: Although clear progress has been made in expanding the availability of precision oncology-based treatment paradigms, our results suggest a continued unmet need for innovative therapeutic strategies, particularly for cancers with currently undruggable oncogenic drivers. See related commentary by Horak and Fröhling, p. 18. This article is featured in Selected Articles from This Issue, p. 5.

Deep Learning Model for Tumor Type Prediction using Targeted Clinical Genomic Sequencing Data.

Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a dataset of 39,787 solid tumors sequenced using a clinical targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivalling performance of WGS-based methods. GDD-ENS can also guide diagnoses on rare type and cancers of unknown primary, and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows has enabled clinically-relevant tumor type predictions to guide treatment decisions in real time.

Diagnostic yield and clinical relevance of expanded genetic testing for cancer patients.

BACKGROUND: Genetic testing (GT) for hereditary cancer predisposition is traditionally performed on selected genes based on established guidelines for each cancer type. Recently, expanded GT (eGT) using large hereditary cancer gene panels uncovered hereditary predisposition in a greater proportion of patients than previously anticipated. We sought to define the diagnostic yield of eGT and its clinical relevance in a broad cancer patient population over a 5-year period. METHODS: A total of 17,523 cancer patients with a broad range of solid tumors, who received eGT at Memorial Sloan Kettering Cancer Center between July 2015 to April 2020, were included in the study. The patients were unselected for current GT criteria such as cancer type, age of onset, and/or family history of disease. The diagnostic yield of eGT was determined for each cancer type. For 9187 patients with five common cancer types frequently interrogated for hereditary predisposition (breast, colorectal, ovarian, pancreatic, and prostate cancer), the rate of pathogenic/likely pathogenic (P/LP) variants in genes that have been associated with each cancer type was analyzed. The clinical implications of additional findings in genes not known to be associated with a patients' cancer type were investigated. RESULTS: 16.7% of patients in a broad cancer cohort had P/LP variants in hereditary cancer predisposition genes identified by eGT. The diagnostic yield of eGT in patients with breast, colorectal, ovarian, pancreatic, and prostate cancer was 17.5%, 15.3%, 24.2%, 19.4%, and 15.9%, respectively. Additionally, 8% of the patients with five common cancers had P/LP variants in genes not known to be associated with the patient's current cancer type, with 0.8% of them having such a variant that confers a high risk for another cancer type. Analysis of clinical and family histories revealed that 74% of patients with variants in genes not associated with their current cancer type but which conferred a high risk for another cancer did not meet the current GT criteria for the genes harboring these variants. One or more variants of uncertain significance were identified in 57% of the patients. CONCLUSIONS: Compared to targeted testing approaches, eGT can increase the yield of detection of hereditary cancer predisposition in patients with a range of tumors, allowing opportunities for enhanced surveillance and intervention. The benefits of performing eGT should be weighed against the added number of VUSs identified with this approach.

The context-specific role of germline pathogenicity in tumorigenesis.

Human cancers arise from environmental, heritable and somatic factors, but how these mechanisms interact in tumorigenesis is poorly understood. Studying 17,152 prospectively sequenced patients with cancer, we identified pathogenic germline variants in cancer predisposition genes, and assessed their zygosity and co-occurring somatic alterations in the concomitant tumors. Two major routes to tumorigenesis were apparent. In carriers of pathogenic germline variants in high-penetrance genes (5.1% overall), lineage-dependent patterns of biallelic inactivation led to tumors exhibiting mechanism-specific somatic phenotypes and fewer additional somatic oncogenic drivers. Nevertheless, 27% of cancers in these patients, and most tumors in patients with pathogenic germline variants in lower-penetrance genes, lacked particular hallmarks of tumorigenesis associated with the germline allele. The dependence of tumors on pathogenic germline variants is variable and often dictated by both penetrance and lineage, a finding with implications for clinical management.

Enhanced specificity of clinical high-sensitivity tumor mutation profiling in cell-free DNA via paired normal sequencing using MSK-ACCESS.

Circulating cell-free DNA from blood plasma of cancer patients can be used to non-invasively interrogate somatic tumor alterations. Here we develop MSK-ACCESS (Memorial Sloan Kettering - Analysis of Circulating cfDNA to Examine Somatic Status), an NGS assay for detection of very low frequency somatic alterations in 129 genes. Analytical validation demonstrated 92% sensitivity in de-novo mutation calling down to 0.5% allele frequency and 99% for a priori mutation profiling. To evaluate the performance of MSK-ACCESS, we report results from 681 prospective blood samples that underwent clinical analysis to guide patient management. Somatic alterations are detected in 73% of the samples, 56% of which have clinically actionable alterations. The utilization of matched normal sequencing allows retention of somatic alterations while removing over 10,000 germline and clonal hematopoiesis variants. Our experience illustrates the importance of analyzing matched normal samples when interpreting cfDNA results and highlights the importance of cfDNA as a genomic profiling source for cancer patients.

Cancer therapy shapes the fitness landscape of clonal hematopoiesis.

Acquired mutations are pervasive across normal tissues. However, understanding of the processes that drive transformation of certain clones to cancer is limited. Here we study this phenomenon in the context of clonal hematopoiesis (CH) and the development of therapy-related myeloid neoplasms (tMNs). We find that mutations are selected differentially based on exposures. Mutations in ASXL1 are enriched in current or former smokers, whereas cancer therapy with radiation, platinum and topoisomerase II inhibitors preferentially selects for mutations in DNA damage response genes (TP53, PPM1D, CHEK2). Sequential sampling provides definitive evidence that DNA damage response clones outcompete other clones when exposed to certain therapies. Among cases in which CH was previously detected, the CH mutation was present at tMN diagnosis. We identify the molecular characteristics of CH that increase risk of tMN. The increasing implementation of clinical sequencing at diagnosis provides an opportunity to identify patients at risk of tMN for prevention strategies.

Development of Genome-Derived Tumor Type Prediction to Inform Clinical Cancer Care.

IMPORTANCE: Diagnosing the site of origin for cancer is a pillar of disease classification that has directed clinical care for more than a century. Even in an era of precision oncologic practice, in which treatment is increasingly informed by the presence or absence of mutant genes responsible for cancer growth and progression, tumor origin remains a critical factor in tumor biologic characteristics and therapeutic sensitivity. OBJECTIVE: To evaluate whether data derived from routine clinical DNA sequencing of tumors could complement conventional approaches to enable improved diagnostic accuracy. DESIGN, SETTING, AND PARTICIPANTS: A machine learning approach was developed to predict tumor type from targeted panel DNA sequence data obtained at the point of care, incorporating both discrete molecular alterations and inferred features such as mutational signatures. This algorithm was trained on 7791 tumors representing 22 cancer types selected from a prospectively sequenced cohort of patients with advanced cancer. RESULTS: The correct tumor type was predicted for 5748 of the 7791 patients (73.8%) in the training set as well as 8623 of 11 644 patients (74.1%) in an independent cohort. Predictions were assigned probabilities that reflected empirical accuracy, with 3388 cases (43.5%) representing high-confidence predictions (>95% probability). Informative molecular features and feature categories varied widely by tumor type. Genomic analysis of plasma cell-free DNA yielded accurate predictions in 45 of 60 cases (75.0%), suggesting that this approach may be applied in diverse clinical settings including as an adjunct to cancer screening. Likely tissues of origin were predicted from targeted tumor sequencing in 95 of 141 patients (67.4%) with cancers of unknown primary site. Applying this method prospectively to patients under active care enabled genome-directed reassessment of diagnosis in 2 patients initially presumed to have metastatic breast cancer, leading to the selection of more appropriate treatments, which elicited clinical responses. CONCLUSIONS AND RELEVANCE: These results suggest that the application of artificial intelligence to predict tissue of origin in oncologic practice can act as a useful complement to conventional histologic review to provide integrated pathologic diagnoses, often with important therapeutic implications.

Widespread Selection for Oncogenic Mutant Allele Imbalance in Cancer.

Driver mutations in oncogenes encode proteins with gain-of-function properties that enhance fitness. Heterozygous mutations are thus viewed as sufficient for tumorigenesis. We describe widespread oncogenic mutant allele imbalance in 13,448 prospectively characterized cancers. Imbalance was selected for through modest dosage increases of gain-of-fitness mutations. Negative selection targeted haplo-essential effectors of the spliceosome. Loss of the normal allele comprised a distinct class of imbalance driven by competitive fitness, which correlated with enhanced response to targeted therapies. In many cancers, an antecedent oncogenic mutation drove evolutionarily dependent allele-specific imbalance. In other instances, oncogenic mutations co-opted independent copy-number changes via the evolutionary process of exaptation. Oncogenic allele imbalance is a pervasive evolutionary innovation that enhances fitness and modulates sensitivity to targeted therapy.

Clinical Sequencing Defines the Genomic Landscape of Metastatic Colorectal Cancer.

Metastatic colorectal cancers (mCRCs) are clinically heterogeneous, but the genomic basis of this variability remains poorly understood. We performed prospective targeted sequencing of 1,134 CRCs. We identified splice alterations in intronic regions of APC and large in-frame deletions in CTNNB1, increasing oncogenic WNT pathway alterations to 96% of CRCs. Right-sided primary site in microsatellite stable mCRC was associated with shorter survival, older age at diagnosis, increased mutations, and enrichment of oncogenic alterations in KRAS, BRAF, PIK3CA, AKT1, RNF43, and SMAD4 compared with left-sided primaries. Left-sided tumors frequently had no identifiable genetic alteration in mitogenic signaling, but exhibited higher mitogenic ligand expression. Our results suggest different pathways to tumorigenesis in right- and left-sided microsatellite stable CRC that may underlie clinical differences.

Mutation Detection in Patients With Advanced Cancer by Universal Sequencing of Cancer-Related Genes in Tumor and Normal DNA vs Guideline-Based Germline Testing.

Importance: Guidelines for cancer genetic testing based on family history may miss clinically actionable genetic changes with established implications for cancer screening or prevention. Objective: To determine the proportion and potential clinical implications of inherited variants detected using simultaneous sequencing of the tumor and normal tissue ("tumor-normal sequencing") compared with genetic test results based on current guidelines. Design, Setting, and Participants: From January 2014 until May 2016 at Memorial Sloan Kettering Cancer Center, 10 336 patients consented to tumor DNA sequencing. Since May 2015, 1040 of these patients with advanced cancer were referred by their oncologists for germline analysis of 76 cancer predisposition genes. Patients with clinically actionable inherited mutations whose genetic test results would not have been predicted by published decision rules were identified. Follow-up for potential clinical implications of mutation detection was through May 2017. Exposure: Tumor and germline sequencing compared with the predicted yield of targeted germline sequencing based on clinical guidelines. Main Outcomes and Measures: Proportion of clinically actionable germline mutations detected by universal tumor-normal sequencing that would not have been detected by guideline-directed testing. Results: Of 1040 patients, the median age was 58 years (interquartile range, 50.5-66 years), 65.3% were male, and 81.3% had stage IV disease at the time of genomic analysis, with prostate, renal, pancreatic, breast, and colon cancer as the most common diagnoses. Of the 1040 patients, 182 (17.5%; 95% CI, 15.3%-19.9%) had clinically actionable mutations conferring cancer susceptibility, including 149 with moderate- to high-penetrance mutations; 101 patients tested (9.7%; 95% CI, 8.1%-11.7%) would not have had these mutations detected using clinical guidelines, including 65 with moderate- to high-penetrance mutations. Frequency of inherited mutations was related to case mix, stage, and founder mutations. Germline findings led to discussion or initiation of change to targeted therapy in 38 patients tested (3.7%) and predictive testing in the families of 13 individuals (1.3%), including 6 for whom genetic evaluation would not have been initiated by guideline-based testing. Conclusions and Relevance: In this referral population with selected advanced cancers, universal sequencing of a broad panel of cancer-related genes in paired germline and tumor DNA samples was associated with increased detection of individuals with potentially clinically significant heritable mutations over the predicted yield of targeted germline testing based on current clinical guidelines. Knowledge of these additional mutations can help guide therapeutic and preventive interventions, but whether all of these interventions would improve outcomes for patients with cancer or their family members requires further study. Trial Registration: clinicaltrials.gov Identifier: NCT01775072.

Therapy-Related Clonal Hematopoiesis in Patients with Non-hematologic Cancers Is Common and Associated with Adverse Clinical Outcomes.

Clonal hematopoiesis (CH), as evidenced by recurrent somatic mutations in leukemia-associated genes, commonly occurs among aging human hematopoietic stem cells. We analyzed deep-coverage, targeted, next-generation sequencing (NGS) data of paired tumor and blood samples from 8,810 individuals to assess the frequency and clinical relevance of CH in patients with non-hematologic malignancies. We identified CH in 25% of cancer patients, with 4.5% harboring presumptive leukemia driver mutations (CH-PD). CH was associated with increased age, prior radiation therapy, and tobacco use. PPM1D and TP53 mutations were associated with prior exposure to chemotherapy. CH and CH-PD led to an increased incidence of subsequent hematologic cancers, and CH-PD was associated with shorter patient survival. These data suggest that CH occurs in an age-dependent manner and that specific perturbations can enhance fitness of clonal hematopoietic stem cells, which can impact outcome through progression to hematologic malignancies and through cell-non-autonomous effects on solid tumor biology.

Prospective Genomic Profiling of Prostate Cancer Across Disease States Reveals Germline and Somatic Alterations That May Affect Clinical Decision Making.

PURPOSE: A long natural history and a predominant osseous pattern of metastatic spread are impediments to the adoption of precision medicine in patients with prostate cancer. To establish the feasibility of clinical genomic profiling in the disease, we performed targeted deep sequencing of tumor and normal DNA from patients with locoregional, metastatic non-castrate, and metastatic castration-resistant prostate cancer (CRPC). METHODS: Patients consented to genomic analysis of their tumor and germline DNA. A hybridization capture-based clinical assay was employed to identify single nucleotide variations, small insertions and deletions, copy number alterations and structural rearrangements in over 300 cancer-related genes in tumors and matched normal blood. RESULTS: We successfully sequenced 504 tumors from 451 patients with prostate cancer. Potentially actionable alterations were identified in DNA damage repair (DDR), PI3K, and MAP kinase pathways. 27% of patients harbored a germline or a somatic alteration in a DDR gene that may predict for response to PARP inhibition. Profiling of matched tumors from individual patients revealed that somatic TP53 and BRCA2 alterations arose early in tumors from patients who eventually developed metastatic disease. In contrast, comparative analysis across disease states revealed that APC alterations were enriched in metastatic tumors, while ATM alterations were specifically enriched in CRPC. CONCLUSION: Through genomic profiling of prostate tumors representing the disease clinical spectrum, we identified a high frequency of potentially actionable alterations and possible drivers of disease initiation, metastasis and castration-resistance. Our findings support the routine use of tumor and germline DNA profiling for patients with advanced prostate cancer, for the purpose of guiding enrollment in targeted clinical trials and counseling families at increased risk of malignancy.

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.

Comprehensive detection of germline variants by MSK-IMPACT, a clinical diagnostic platform for solid tumor molecular oncology and concurrent cancer predisposition testing.

BACKGROUND: The growing number of Next Generation Sequencing (NGS) tests is transforming the routine clinical diagnosis of hereditary cancers. Identifying whether a cancer is the result of an underlying disease-causing mutation in a cancer predisposition gene is not only diagnostic for a cancer predisposition syndrome, but also has significant clinical implications in the clinical management of patients and their families. METHODS: Here, we evaluated the performance of MSK-IMPACT (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets) in detecting genetic alterations in 76 genes implicated in cancer predisposition syndromes. Output from hybridization-based capture was sequenced on an Illumina HiSeq 2500. A custom analysis pipeline was used to detect single nucleotide variants (SNVs), small insertions/deletions (indels) and copy number variants (CNVs). RESULTS: MSK-IMPACT detected all germline variants in a set of 233 unique patient DNA samples, previously confirmed by previous single gene testing. Reproducibility of variant calls was demonstrated using inter- and intra- run replicates. Moreover, in 16 samples, we identified additional pathogenic mutations other than those previously identified through a traditional gene-by-gene approach, including founder mutations in BRCA1, BRCA2, CHEK2 and APC, and truncating mutations in TP53, TSC2, ATM and VHL. CONCLUSIONS: This study highlights the importance of the NGS-based gene panel testing approach in comprehensively identifying germline variants contributing to cancer predisposition and simultaneous detection of somatic and germline alterations.

Genetic Determinants of Cisplatin Resistance in Patients With Advanced Germ Cell Tumors.

Purpose Owing to its exquisite chemotherapy sensitivity, most patients with metastatic germ cell tumors (GCTs) are cured with cisplatin-based chemotherapy. However, up to 30% of patients with advanced GCT exhibit cisplatin resistance, which requires intensive salvage treatment, and have a 50% risk of cancer-related death. To identify a genetic basis for cisplatin resistance, we performed whole-exome and targeted sequencing of cisplatin-sensitive and cisplatin-resistant GCTs. Methods Men with GCT who received a cisplatin-containing chemotherapy regimen and had available tumor tissue were eligible to participate in this study. Whole-exome sequencing or targeted exon-capture-based sequencing was performed on 180 tumors. Patients were categorized as cisplatin sensitive or cisplatin resistant by using a combination of postchemotherapy parameters, including serum tumor marker levels, radiology, and pathology at surgical resection of residual disease. Results TP53 alterations were present exclusively in cisplatin-resistant tumors and were particularly prevalent among primary mediastinal nonseminomas (72%). TP53 pathway alterations including MDM2 amplifications were more common among patients with adverse clinical features, categorized as poor risk according to the International Germ Cell Cancer Collaborative Group (IGCCCG) model. Despite this association, TP53 and MDM2 alterations predicted adverse prognosis independent of the IGCCCG model. Actionable alterations, including novel RAC1 mutations, were detected in 55% of cisplatin-resistant GCTs. Conclusion In GCT, TP53 and MDM2 alterations were associated with cisplatin resistance and inferior outcomes, independent of the IGCCCG model. The finding of frequent TP53 alterations among mediastinal primary nonseminomas may explain the more frequent chemoresistance observed with this tumor subtype. A substantial portion of cisplatin-resistant GCTs harbor actionable alterations, which might respond to targeted therapies. Genomic profiling of patients with advanced GCT could improve current risk stratification and identify novel therapeutic approaches for patients with cisplatin-resistant disease.

Translational Bioinformatics and Clinical Research (Biomedical) Informatics.

Translational bioinformatics and clinical research (biomedical) informatics are the primary domains related to informatics activities that support translational research. Translational bioinformatics focuses on computational techniques in genetics, molecular biology, and systems biology. Clinical research (biomedical) informatics involves the use of informatics in discovery and management of new knowledge relating to health and disease. This article details 3 projects that are hybrid applications of translational bioinformatics and clinical research (biomedical) informatics: The Cancer Genome Atlas, the cBioPortal for Cancer Genomics, and the Memorial Sloan Kettering Cancer Center clinical variants and results database, all designed to facilitate insights into cancer biology and clinical/therapeutic correlations.

Translational Bioinformatics and Clinical Research (Biomedical) Informatics.

Translational bioinformatics and clinical research (biomedical) informatics are the primary domains related to informatics activities that support translational research. Translational bioinformatics focuses on computational techniques in genetics, molecular biology, and systems biology. Clinical research (biomedical) informatics involves the use of informatics in discovery and management of new knowledge relating to health and disease. This article details 3 projects that are hybrid applications of translational bioinformatics and clinical research (biomedical) informatics: The Cancer Genome Atlas, the cBioPortal for Cancer Genomics, and the Memorial Sloan Kettering Cancer Center clinical variants and results database, all designed to facilitate insights into cancer biology and clinical/therapeutic correlations.