Next-Generation Sequencing-Based Evaluation of the Actionable Mutational Landscape in Solid Tumors: the “MOZART” Prospective Observational Study

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We performed a molecular profiling of metastatic solid tumors utilizing a comprehensive next-generation sequencing (NGS) assay to determine mutations’ type, frequency and actionability and potential correlations with PD-L1 expression. Methods 304 adult patients with heavily-pretreated metastatic cancers treated between 01/2019-03/2021 were recruited. The CLIA-/UKAS-accredit Oncofocus® assay targeting 505 genes was used on newly-obtained or archived biopsies. Chi-square, Kruskal-Wallis and Wilcoxon rank-sum test were used where appropriate. Results were significant for p < 0.05. Results A total of 237 tumors (78%) harbored actionable mutations. Tumors were positive for PD-L1 in 68.9% cases. The median number of mutant genes/tumor was of 2.0 (IQR: 1.0–3.0). Only 34.5% were actionable ESCAT Tier I-II with different prevalence according to cancer type. The DNA damage repair (14%), the PI3K/AKT/mTOR (14%) and the RAS/RAF/MAPK (12%) pathways were the most frequently altered. No association was found between PD-L1, ESCAT, age, sex and tumor mutational status. Sixty-two patients underwent targeted treatment, with 37.1% obtaining objective responses. Conclusions We highlight the clinical value of molecular profiling in metastatic solid tumors using comprehensive NGS-based panels to improve treatment algorithms in situations of uncertainty and facilitate clinical trial recruitment. solid tumors mutation ESCAT clinical actionability molecular profiling metastatic Figures Figure 1 Figure 2 Figure 3 Introduction When chemotherapy was first introduced for the treatment of solid tumors, the new and unexpected survival benefits observed prioritized the need to develop less toxic and more targeted therapeutic approaches 1 . Subsequently, improved screening programs, novel systemic treatments, refinement of surgical and radiotherapeutic techniques, along with more integrated multidisciplinary therapeutic approaches, have led to a visible increase in patients’ survival in the last 3 decades, despite geographical disparities and differences according to cancer type 2 . This has led the scientific community to pursue the identification of increasingly personalized treatments to maximize therapeutic efficacy while sparing patients with unnecessary toxic treatments. In this perspective, a recent exponential increase in both tumor molecular profiling and discovery of effective molecularly targeted agents has provided new therapeutic opportunities for patients with advanced cancers harboring specific somatic and/or germline mutations 3 . Examples are represented by PARP inhibitors (PARPi) in germline BRCA 1/2-mutant ovarian, breast, prostate or pancreatic cancer 4 , the PI3K-inhibitor (PI3Ki) alpelisib for PIK3CA -mutant hormone receptor-positive (HR+)/HER2-negative breast cancer (BC), vemurafenib for BRAF -mutant melanoma 5 or the tyrosine kinase inhibitors (TKI) entrectinib and larotrectinib, for solid tumors carrying NTRK fusions 6 . The identification of the most appropriate targeted therapies is challenging as a consequence of the limited coverage of most molecular profiling tests used in routine clinical practice and the frequent lack of direct evidence-based linkage with therapy 3 . These technical limitations translate into the loss of potential therapeutic opportunities for many patients. At our center, we have addressed this challenge by performing precision oncology genomic testing utilizing a comprehensive precision oncology next generation sequencing (NGS) assay 7 , 8 , to identify the prevalence of potentially actionable mutations in a large cohort of patients with heavily-pretreated metastatic solid tumors, where therapeutic options are usually limited. Here we report the analysis of test trending data from this large screening programme to investigate the prevalence of actionable mutations directed at either the prescription of specific salvage targeted treatments (off-label or in indication) or alternatively to recruit patients to the most appropriate clinical trials. The NGS genomic data was also analyzed in combination with a PD-L1 companion diagnostic (CDx) immunohistochemistry (IHC) test, in order to assess potential correlations between clinically actionable tumor mutations and PD-L1 expression, a biomarker of response to anti-PD1/PD-L1 immune-checkpoint inhibitors (ICI) 9 . Materials and Methods Study design and objectives This was a prospective observational trial, named “the MOZART Trial”, that recruited adult patients with advanced solid tumors treated at the Oncology Unit of the Cremona Hospital (Italy) between 01/2019 and 03/2021. Patients had to be already pretreated with all available therapies for their metastatic disease and present with at least one accessible metastatic lesion to perform a biopsy. If not, archived formalin-fixed paraffin-embedded (FFPE) tumor tissue from the latest biopsy received was used ( Supplementary methods ). The NGS and PD-L1 assays were applied to the tumor biopsies as described below. The primary objective of the present observational study, was to assess the prevalence of actionable molecular alterations in solid tumors in later treatment lines. Secondary objectives were to detect the levels of PD-L1 and potential association with specific actionable mutations. To guide therapeutic decision-making was not in the purpose of the present study. However, treating physicians were informed of the test results. If patients were deemed to be sufficiently fit to receive a further treatment line, physicians could then ask permission to their Institution for off-label prescription or compassionate drug use, unless an on-label matched mutation-treatment was available. If possible, patients were recruited in clinical trials, as well. PD-L1 assessment for immunotherapy prescription was repeated with an approved PD-L1 CDx assay whenever required, according to tumor type, treatment line and regulatory approval in Italy. Comprehensive NGS genomic profiling NGS genomic profiling including DNA extraction, library preparation, sequencing and data analysis was centrally performed on FFPE tumor tissue. The Oncofocus® assay (Oncologica® UK Ltd, Chesterford Research Park, Cambridge, UK), specifically adopted for the present molecular screening, is validated for clinical use and accredited by CLIA (ID 99D2170813) and by UKAS (9376) in compliance with ISO15189:2012 and following the guidelines published by the Association for Molecular Pathology and College of American Pathologists and IQN-Path ASBL 10 , 11 . It is a NGS-based platform designed to detect druggable mutations, according to Food and Drug Administration (FDA) and European Medicine Agency (EMA) approvals, European Society for Medical Oncology (ESMO) and National Comprehensive Cancer Network (NCCN) guideline references or ongoing clinical trials worldwide and is updated every 12 weeks 7 , 8 . It targets 505 genes and detects actionable genetic variants linked to 700 anti-cancer targeted therapies or combinations. For the purpose of this study, all genomic alterations detected were manually classified by using the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT), according to the latest guidelines and evidence found 12 ( Supplementary references ). DNA and RNA extraction, library preparation and sequencing, quality control metrics and data analysis methods are reported as Supplementary methods. Immunohistochemistry for PD-L1 expression and tumor mutational burden assessment The assessment of PD-L1 immunostaining was performed by a qualified histopathologist at Oncologica® (G.W.), in accordance with PD-L1 clinical reporting guidelines 13 , to quantify the proportion of tumor cells expressing PD-L1 (tumor proportion score) and the area occupied by tumor infiltrating PD-L1 positive immune cells in FFPE samples. According to the tumor proportional score (TPS), all cases were subdivided in PD-L1 negative (TPS < 1%), positive-low (TPS ≤ 1%≤TPS ≤ 49%) and positive-high (TPS ≥ 50%) 14,15 . The rabbit monoclonal antibody clone E1L3N used for PD-L1 assessment is not licensed and approved for use in clinical testing to direct the use of PD1/PD-L1 therapies ( Supplementary methods ). Tumor mutational burden (TMB) is usually defined as the number of somatic mutations per megabase (Mb) of interrogated genomic sequence and is a potential biomarker of response to ICI 9 . The Oncofocus assay covers ~ 1.2 Mb over 505 genes, complying with the evidence-based suggestion that gene panels of at least 1 Mb are needed for TMB measurement 16 – 18 . Ten mutations/Mb was the cut-off to define high vs. low TMB cases. Statistical analysis Descriptive statistics, Chi-square, Kruskal-Wallis or Wilcoxon rank-sum test with continuity correction were used where appropriate. Univariate logistic regressions were performed to assess associations between mutational or PD-L1 status and variables of interest. Statistical significance was set at p < 0.05. For all analyses Microsoft Excel 2019 vers.16.50 and R vers.3.6.1 for MacOS X were used. Results Population characteristics Overall, 304 patients were included in this study. Median age was 56.3 years (interquartile range [IQR]: 59.0–68.4) and patients were predominantly female (59.5%). All patients were sufficiently fit (ECOG 0–2) to be treated with anticancer agents and were candidates to receive a salvage therapy or best supportive care (BSC) in the absence of suitable late-line treatments. A total of 237 tumors (78%) harbored potentially actionable mutations detected with the NGS assay platform. BC was the most common tumor type, representing 25.3% of the entire cohort, followed by colorectal cancer (CRC) (12.5%), ovarian cancer (7.9%), prostate cancer (6.9%), primary malignancies of the central nervous system (6.6%) and non small cell lung cancer (NSCLC) (5.3%). With a prevalence 2%, there were patients affected by bladder, pancreatic, kidney, biliary tract, cervical/vulvovaginal cancer, head and neck (H&N) tumors, soft-tissues sarcomas from several origins (abdominal, H&N, lung) and gastric cancer. Tumors with a prevalence ≤ 2% in our cohort were small cell lung cancer (SCLC), endometrial, liver, non-melanoma skin cancers, melanoma, mesothelioma, cancer of unknown primary (CUP), neuroendocrine tumors (NET) from distinct origins (pancreas, lung, intestine, unknown), thymic carcinoma and penile cancer. The main population characteristics are detailed in Table 1 . Table 1 Patients characteristics CHARACTERISTICS OVERALL POPULATION N % 304 100.0 Patients' Age Median 56.3 - IQR 59.0-68.4 - Sex Female 181 59.5 Male 123 40.5 Cancer Type* Carcinomas 268 88.2 Non-carcinomas (excluding hematologic malignancies) 36 11.8 Mutational Status Mutant 237 78.0 Wild-Type 67 22.0 Mutation per Tumor Median 2 - IQR 1–3 - Range 0–8 - PD-L1 Status Median % 1.0 - IQR 0.0–3.0 - Negative (TPS < 1%) 87 31.1 Positive-low (1%≤TPS < 49%) 173 61.8 Positive-high(TPS ≥ 50%) 20 7.1 Overall 280 92.1 Legend. TPS: tumor proportion score; IQR: interquartile range; CNS: central nervous system; NSCLC: non-small cell lung cancer; SCLC: small cell lung cancer; CUP: cancer of unknown primary, TPS: tumor proportion score. *: cancer types and frequency are detailed in Table 2 . Tumors were positive for PD-L1 according to TPS in 68.9% cases, with high levels (TPS ≥ 50%) observed in 7.1%. In 24 cases, the material to analyze both PD-L1 and tumor mutational status was insufficient. In these cases, the NGS assay was prioritized. The distribution of PD-L1-positive (+) vs. PD-L1-negative (-) tumors across cancer types did not reach the statistical significance ( p = 0.065) (Fig. 1 ). Still, two macro groups with significant difference in PD-L1 status could be ultimately identified ( p < 0.001). Namely, a group including BC, CNS, genitourinary/prostate (GU) and Gynecological malignancies (80% PD-L1+) and a group including gastrointestinal (GI), liver, pancreatic, biliary tract, pulmonary, pleural, H&N, skin and rare tumors (61% PD-L1+). Molecular results Overall, 236 tumors harbored at least one mutation in genes screened by the Oncofocus® panel (Table 2 ). Table 2 Detailed cancer type incidence in the study cohort CHARACTERISTICS OVERALL POPULATION MUTANT POPULATION N %* N % # 304 100.0 236 77.6 Cancer Type Breast 77 25.3 62 80.5 Colorectal, anal and small intestine 38 12.5 36 94.7 Ovarian 24 7.9 16 66.7 Prostate 21 6.9 7 33.3 CNS Primary 20 6.6 18 90.0 NSCLC 16 5.3 14 87.5 Bladder/ureter 9 4.9 8 88.9 Pancreas 15 3.3 13 86.7 Kidney 10 3.0 7 70.0 Gallbladder and biliary tract 9 3.0 6 66.7 Cervix/Vagina 7 3.0 4 57.1 Sarcoma 9 2.3 6 66.7 Head and Neck 7 2.3 5 71.4 Gastric 7 2.3 6 85.7 SCLC 5 2.0 5 100.0 Liver 6 1.6 4 66.7 Mesothelioma 4 1.6 3 75.0 Uterine 5 1.3 5 100.0 Melanoma 3 1.3 3 100.0 Skin non-melanoma 2 1.3 1 50.0 CUP 4 1.0 2 50.0 Neuroendocrine 4 0.7 4 100.0 Thymic 1 0.3 1 100.0 Penis 1 0.3 0 0.0 Legend. CNS: central nervous system; NSCLC: non-small cell lung cancer; SCLC: small cell lung cancer; CUP: cancer of unknown primary. *: The proportions in the overall study cohort are referred to the frequency of each specific cancer type with respect to the total population of 304 patients. #: The proportions in the mutant study cohort are referred to the total of each cancer type. A total of 549 mutations were detected (full list available as Supplementary Table 1 ), along with 1 case of high TMB (TMB-H) in a NSCLC. In multiple cases the same mutation was detected in different tumors, leading to a total of 272 distinct mutations. The median number of mutant genes per tumor was of 2.0 (IQR: 1.0–3.0, minimum-maximum range [min-max]: 0.0–8.0). The median mutational frequency rate was 77.8% (IQR: 66.7% − 92.1%; min-max: 0.0% − 100.0%). The tumors in the lower quartile (Q1) of mutational frequency were ovarian, gallbladder/biliary tract, liver, cervix/vaginal, non-melanoma skin cancers, sarcomas, CUPs, penis and prostate cancer. In the following quartile (Q2) were included mesothelioma, kidney and H&N cancers, whilst in the subsequent quartile (Q3) BC, NSCLC, CNS primary tumors, bladder/ureter, pancreatic and gastric cancer were included. The upper quartile (Q4) included colorectal/anal/small intestine, uterine, thymic, neuroendocrine cancers, SCLC and melanomas. As expectable, the difference in mutant vs. non-mutant tumors for each quartile group was significantly higher as the quartile increased (p < 0.001), with an increasing proportion per quartile of cases harbouring at least one mutation in covered genes (Table 3 ). Table 3 Proportion of mutant tumors per mutational frequency quartiles and distribution of actionable mutations per quartile MUTATIONAL STATUS Q1 Q2 Q3 Q4 P N % N % N % N % Mutant tumors 46 55.4 15 71.4 121 84.0 54 96.4 < 0.001 Non-mutant tumors 37 44.6 6 28.6 23 16.0 2 3.6 Total number of patients 83 27.3 21 6.9 144 47.4 56 18.4 ESCAT I-II mutations 6 5.9 1 3.2 69 30.1 5 4.2 66.7% and ≤ 77.8% mutant tumors per cancer type; Q3: >77.8% and ≤ 91.2% mutant tumors per cancer type; Q4: >91.2% and ≤ 100.0% mutant tumors per cancer type. Consistently, a statistically significant difference in the median number of mutations according to tumor type was observed ( p < 0.001), with CNS tumors showing a median of 3.0 (IQR: 1.75–3.25), followed by GI tumors presenting with a median of 2.0 (IQR: 2.0–3.0), breast tumors showing a median of 2.0 altered genes per tumor (IQR: 1.0–3.0), liver, pancreatic and biliary tract cancers exhibiting a median of 2.0 (IQR: 1.0–2.0). No difference in mutation number according to sex and age ( p = 0.279 and p = 0.098 respectively) was observed. The pathways most frequently involved were related to DNA damage repair (14%), the PI3K/AKT/mTOR (14%) and the RAS/RAF/MAPK (12%) pathways. Moreover, many genes involved in cell cycle control and gene expression regulation were found mutated (32% cases). In fact, the most frequently altered gene was TP53 , mutated in 20.2% patients, followed by BRCA2 (7.2%), PIK3CA (7%), KRAS ( 6.6%), ARID1A (3.5%), NF1 (3.5%) and ATM (3.1%) (Fig. 2 ). Alterations in cancer-related genes were derived from a range of different mechanisms, including substitution, deletion, amplification, fusion, insertion, chromosomic aberration, deletion/insertion, exon skipping and pathogenetic gene variants. Substitutions were the predominant mutation type, detected in 49.3% of the samples, followed by deletions (16.8%), amplifications (9.5%), gene fusions (4.7%), insertions (2.6%), chromosomic aberrations (1.1%) and other types of mutations with lower frequency but collectively representing 14.6% of cases (Fig. 2 ). Regarding PD-L1, we did not detect any association between PD-L1 status (positive vs. negative) and tumor mutational status ( p = 0.454) or a difference in mutation number according to PD-L1 category ( p = 0.717 ). PD-L1 TPS score as continuous variable was also not associated to mutational status ( p = 0.840). Actionability of the detected mutations When considering the ESCAT scale to evaluate the clinical actionability of our cohort’s mutational landscape, we observed that among the total 273 different alterations identified (including TMB-H), only 19.4% were considerable as Tier I for at least one cancer type, while 16.1% were ESCAT Tier II, 6.6% ESCAT Tier III and the rest was Tier IV-V or X (Fig. 3 ). When we matched the genomic alteration with the respective ESCAT Tier according to the tumor where the mutation was actually detected, we observed that mutant non-breast tumors were significantly less likely to be associated with ESCAT Tier I-II mutations ( p 10%) were detected only for BC (77.9%), melanoma (66.7%), prostate cancer (44.4%) and NSCLC (11.8%). ESCAT Tier I-II mutations were found also in H&N (6.7%), bladder (6.7%), gastric (5.9%), CNS (5.7%) ovarian (4.8%) and CRC (3.6%) in < 7% of cases. In the remaining tumor types, only ESCAT Tier III-V or X mutations were found (Fig. 3 ). Consistently, when subdividing cancer types according to mutational frequency, a higher proportion of mutations was not necessarily a synonym of actionability, since the vast majority of actionable ESCAT-I/II mutations were found in the Q3 group (30.1%; p < 0.001) rather than Q4. In fact, the proportion of actionable mutations per group was low and similar between Q1, Q2 and Q4 (5.9, 3.2, 4.2%, respectively) (Table 3 ). The Q3 group included BC, NSCLC, CNS primary tumors, bladder/ureter, pancreatic and gastric cancer; the higher actionability was driven by BC and NSCLC (Fig. 3 ). Finally, no association was found between PD-L1 status and the presence of ESCAT tier I-II mutations ( p = 0.148). Examples of matched treatments We could only retrospectively retrieve data on clinical practice therapeutic decision-making and outcomes from patients’ charts. Overall, 62 out of 304 (20.4%) patients were treated according to the genomic assay or PD-L1 test result through an on-label, off-label or compassionate use prescribing procedure, or in the context of a clinical trial. When required, PD-L1 positivity was confirmed with an approved CDx. Twenty-three (37.1%) patients obtained the most relevant clinical benefit, achieving partial or complete responses as their best response according to clinical judgement, with a median progression-free interval (PFI) of 32.0 months (95% confidence interval [CI]: 18.5 months – 84.0 months). These patients were affected by either BC, endometrial cancer, mesothelioma or glioblastoma. In these tumors, the test identified from one to two mutations or PD-L1 positivity. The other 39 tested cancers (bladder, gallbladder, endometrial, lung, prostate, parathyroid, ovary, melanoma, sarcoma) obtained either disease progression or stability as their best response, with a median PFI of 16.0 months (95%CI: 6.0 months – 23.0 months). In these tumors, the test detected a PD-L1 positivity and/or several mutations, suggesting the presence of different “passenger” genomic alterations, likely not directly involved into cancer progression and responsiveness to specific target therapies. Two patients with different tumors harboring the same mutation and both treated with off-label abemaciclib gave consent to report their non-aggregated clinical outcomes. A first case, was a chemotherapy-refractory, CDKN2B -deleted metastatic parotid tumors, that received abemaciclib as fourth-line therapy. In this setting the inhibition resulted in a PFI of 18.5 months and improved quality of life. The second case was a chemotherapy-refractory, CDKN2B -deleted metastatic endometrial adenocarcinoma treated with abemaciclib as third-line therapy. In this setting the inhibition resulted in a poor PFI of 2.5 months, with mild improvement in quality of life. Discussion Our MOZART prospective observational study performed at the Cremona Hospital has demonstrated that precision oncology molecular profiling in advanced solid malignancies is of potential clinical value. The overall mutation detection rate was 78% with clinically actionable mutations of ESCAT Tier I-II detected in 35.5% cases with an additional 6.6% falling within the ESCAT Tier III category. BC, prostate cancer, melanoma and NSCLC were the most frequently mutated tumors with clinically actionable mutations of ESCAT Tier I-II, followed by H&N, bladder, gastric, CNS, ovarian and CRC. No associations were observed between PD-L1 status, age, sex and tumor mutational status. GI and biliary tract tumors were the cancer types most frequently associated with PD-L1 positivity. The genetic variants described in our study belong to some of the most common key cancer regulatory networks in solid tumors, such as the RAS/RAF/MAPK and PI3K/AKT/mTOR signaling pathways, the DNA damage repair (DDR) pathways and cell cycle checkpoints. Notably, many approved or experimental targeted agents are precisely directed against these targets, for example the PI3K inhibitors in BC, mTOR inhibitors in breast and kidney cancer, AKT inhibitors in BC, BRAF and MEK inhibitors in melanoma or PARP inhibitors in several solid tumors 4 , 19 – 23 . However, these drugs are costly, present with several side effects and are often administered without identification of the altered pathway of interest (e.g. everolimus or capivasertib). This relates to the fact that the regulatory agencies have granted approval for these targeted agents without requirement for an accompanying CDx. Taking into account that their correct positioning in the therapeutic algorithms frequently present many uncertainties, a broader implementation of molecular testing would greatly improve selection of patients for these targeted agents and establish more personalized therapeutic algorithms when such uncertainty exists. The rate of patients with ESCAT levels I-III identified in this study was comparable with other published screening programs 10 , 24 – 26 . The ESCAT framework was promoted by a group of experts who reached a consensus on the criteria to define the clinical actionability of somatic mutations in solid tumors culminating in the establishment of the ESMO-ESCAT scale 12 . In this way, clinicians now have the opportunity to become familiar with the type of genomic variants reported by genomic tests and their relevance for treatment. This is especially important following the results from the SHIVA and the SAFIR01 trials which both showed no improvement in progression-free survival (PFS) 27 and disappointing overall response rates (ORR) of only 9% 28 in pretreated patients with advanced solid tumors, when the drug-genomic alteration match was not guided by sufficient evidence of clinical activity or efficacy. In fact, in our cohort, only 20% patients ultimately received a mutation-matched treatment, with less than half experiencing an objective response and a prolonged clinical benefit. From this perspective it is critical to interpret mutations in a tumor type-specific manner. The most relevant example of this kind is the BRAF V600E mutation, which is considered Tier I for melanoma, since effective B-Raf inhibitors like dabrafenib and vemurafenib are already approved in the clinic as standard of care treatments 29 , 30 . Conversely, it is considered Tier III for CRC, due to the limited activity of vemurafenib in this particular tumor type 31 , and Tier II in cholangiocarcinoma, following positive results for the combination of dabrafenib and trametinib in the ROAR trial 32 . An exception is represented by specific rare driver mutations that can be targeted by molecular inhibitors approved in tumor-agnostic fashion, like NTRK fusions 33 , 34 , RET fusions 35 and alterations in genes involved in the DNA mismatch repair mechanism 36 . Importantly, a pooled analysis from the SAFIR02-BREAST and SAFIR-PI3K trials showed a significant PFS benefit (60% reduction in the risk of progression and death, p < 0.001) only in the presence of ESCAT Tier I-II alterations 37 , 38 . These results highlight the importance of avoiding an excess of off-label prescription of target drugs, especially if only preclinical evidence of activity exist or only activity/efficacy in other cancer types has been proved. At the same time, targeted treatments with a good preclinical rationale should not be completely discarded in cases with very limited therapeutic options. The two clinical experiences reported within our genomic profiling programme support both the concept that genomic testing for off-label drug prescription should be adopted with caution, as well as the concept that such a genomic profiling programme might represent an opportunity to potentially give access to valuable treatments when the alternatives are scarce. In both cases, abemaciclib, a CDK4/6-inhibitor, was administered based on preclinical and phase I evidence supporting CDKN2B deletions as potential biomarker of efficacy to CDK4/6 pharmacologic inhibition 39 – 41 . Overall, we strongly believe that the implementation of molecular tumor boards (MTB) for the correct interpretation of genomic testing results and their correct implementation in clinical practice, can optimize on-label and off-label target therapy prescriptions, as well as possible recruitment in clinical trials 42 . In fact, MTB have already been introduced in many institutions, resulting in a significant optimization of target therapy prescriptions, as highlighted in several reports, especially for rare and/or complex tumor mutational profiles 43 – 48 . Particularly interesting, in this perspective, is also the Molecular Tumor Board Portal initiative of the Cancer Core Europe consortium, based on a unified legal, scientific and technological platform to share and harness NGS data 44 . The ultimate goal is to automate the interpretation and reporting of complex molecular testing results, adopt a consistent expert-agreed process to systematically link tumor molecular profiles with clinical actions and reduce the need for time-consuming manual procedures, potentially prone to errors 44 . Also, very promising seems to be the implementation of artificial intelligence (AI)-based learning programs to uniform treatment recommendations among different MTB, or directly provide treatment recommendations 49 . Further research is however needed to deliver more solid evidences in this regard. In our study, a high percentage of tumor samples harbored mutations involved in DNA repair mechanisms including TP53 , BRCA1/2 and ATM . Many targeted agents directed towards these pathways are currently investigated in clinical trials or have already entered frontline clinical practice such as PARP inhibitors 4 . In some cases, like PARP inhibitors in BC, approval has been granted in case of germline mutant alterations 4 . Approximately 5–10% of all solid tumors are hereditary and germline pathogenetic variants of these genes are responsible for many of currently known cancer hereditary syndromes 50 . Although somatic genomic testing should not be performed with this purpose, our data suggest that for specific cancers and in selected cases where the oncologic family history is not available, unclear or not particularly suspicious or when appropriate genetic counselling cannot be offered, genomic profiling might also help identifying carriers of germline cancer-associated variants. This is important, considering the implications on treatment for the patient and on cancer prevention for family members 51 . However, suspected inheritable alterations should be further investigated with appropriate testing. Our study has shown that a wide number of structural abnormalities are associated with genomic instability. The majority of mutations identified are considered as nucleotide instabilities (NIN), a type of DNA alteration characterized by an increased frequency of substitutions, deletions, and insertions of one or few nucleotides 52 . Following NIN, chromosomal instability (CIN) was the second most prevalent form of genomic instability identified. These findings are in line with previous studies 53 , 54 . Since CIN is correlated to intrinsic multidrug resistance and poor prognosis, its detection per se might be clinically relevant and represent an additional information for the personalization of cancer care 55 , 56 . It is still unclear how often genomic profiling should be repeated during the disease course because of potential genomic variability between primary and metastatic tumor, as well as between different metastatic sites 57 . For example, within the AURORA molecular screening program an extensive profiling of BC paired primary tumors and metastatic sample was carried out, showing an overall increase in clonality in metastatic samples 58 . Nevertheless, when Van de Haar et al evaluated the differences in the actionable genomic landscape between biopsy pairs longitudinally collected over the treatment course in patients with different metastatic cancers 59 , a full concordance in standard-of-care genomic biomarkers and similar ESCAT tier II mutations’ rate between the first and second biopsy was observed in 99% of the pairs 59 . These results suggest that, while differences might be likely observed between the primary and the metastatic disease in terms of mutational profile, for the majority of metastatic cancer patients, there is a limited evolution of the actionable genome over time. Thus, a single NGS-based analysis on a metastatic sample might be both sufficient and effective to guide treatments or to evaluate a clinical trial enrollment, avoiding the need to perform multiple biopsies over time. Finally, PD-L1 is now used as a major predictive biomarker of response to ICI 9 . Here we aimed at evaluating its association with genomic alterations in solid tumors to speculate on a potential role beyond immunotherapy. However we did not identify any linkages between PD-L1 expression and actionable mutations status in our study cohort. The main limitations of this study relate to the fact that this was performed in a single centre setting, accompanied by the lack of data regarding most mutation-matched treatments and patients survival outcomes, which prevented us from comprehensively assessing the practical impact of genomic profiling. In summary this study highlights the clinical value of molecular profiling in metastatic solid tumors using NGS-based panels. We observed a high overall mutation detection rate, with clinically actionable mutations found in a significant proportion of cases and especially high in BC, prostate cancer, melanoma, and NSCLC. Hence, implementing molecular testing can aid in selecting patients for targeted therapies, improve treatment algorithms in situations of uncertainty and facilitate clinical trial recruitment. Declarations Authors’ contributions Daniele Generali and Francesco Schettini conceived the manuscript. Francesco Schettini carried out the statistical analyses. Francesco Schettini, Daniele Generali and Marianna Sirico wrote the first manuscript draft. All authors except Francesco Schettini and Pablo Rivera were involved in patients’ management or testing. All authors had access to study results, revised and approved the final version of the manuscript. Ethics approval, consent to participate and consent for publication The study protocol was approved by the Local Ethics Committee Val Padana (IRB n.32219) on December 21, 2018. All patients provided written informed consent to participate and consent for publication of the anonymized research results. The study was performed in accordance with the Declaration of Helsinki, Good Clinical Practice (GCP) and local legislation. Data availability Anonymized data are available upon reasonable request from the corresponding authors. Competing interests Francesco Schettini reports honoraria from Novartis, Gilead and Daiichy-Sankyo for educational events/materials and travel expenses from Novartis, Gilead and Daiichy-Sankyo. Daniele Generali declares personal fees for educational events by Novartis, Lilly, Pfizer, Daiichy-Sankyo, Roche; research funds from Astrazeneca, Novartis and LILT. Marco Loddo, Gareth H Williams, Keeda-Marie Hardisty, Paul Scorer and Robert Thatcher are employees of Oncologica UK Ltd. Maurizio Scaltriti is an employee of AstraZeneca. The other authors have nothing to declare. Funding information The study was funded by Mednote, spin-off of the University of Trieste. Acknowledgements Dr. Francesco Schettini is supported by a Rio Hortega clinical scientist contract from the Instituto de Salud Carlos III (ISCIII). Opinions and hypotheses generated are solely of the article’s authors. References DeVita VT, Chu E. A history of cancer chemotherapy. Cancer Res . November 1, 2008;68(21):8643–8653. Allemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nikšić M, et al. Global surveillance of trends in cancer survival 2000-14 (CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet . March 17, 2018;391(10125):1023–1075. 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Therapeutic Implications of Germline Testing in Patients With Advanced Cancers. J Clin Oncol . August 20, 2021;39(24):2698–2709. Lengauer C, Kinzler KW, Vogelstein B. Genetic instabilities in human cancers. Nature . December 17, 1998;396(6712):643–649. Gagos S, Irminger-Finger I. Chromosome instability in neoplasia: chaotic roots to continuous growth. Int J Biochem Cell Biol . May 2005;37(5):1014–1033. Lengauer C, Kinzler KW, Vogelstein B. Genetic instability in colorectal cancers. Nature . April 10, 1997;386(6625):623–627. AJ L, D E, AJ R, A W, NJ B, PA F, et al. Chromosomal instability confers intrinsic multidrug resistance. Cancer research . March 1, 2011;71(5):1858–1870. A W, R H, I T. Association between chromosomal instability and prognosis in colorectal cancer: a meta-analysis. Gut . July 2008;57(7):941–950. Fancello L, Gandini S, Pelicci PG, Mazzarella L. Tumor mutational burden quantification from targeted gene panels: major advancements and challenges. Aftimos P, Oliveira M, Irrthum A, Fumagalli D, Sotiriou C, Gal-Yam EN, et al. Genomic and Transcriptomic Analyses of Breast Cancer Primaries and Matched Metastases in AURORA, the Breast International Group (BIG) Molecular Screening Initiative. Cancer Discov . November 2021;11(11):2796–2811. van de Haar J, Hoes LR, Roepman P, Lolkema MP, Verheul HMW, Gelderblom H, et al. Limited evolution of the actionable metastatic cancer genome under therapeutic pressure. Nat Med . September 2021;27(9):1553–1563. Additional Declarations Competing interest reported. Francesco Schettini reports honoraria from Novartis, Gilead and Daiichy-Sankyo for educational events/materials and travel expenses from Novartis, Gilead and Daiichy-Sankyo. Daniele Generali declares personal fees for educational events by Novartis, Lilly, Pfizer, Daiichy-Sankyo, Roche; research funds from Astrazeneca, Novartis and LILT. Marco Loddo, Gareth H Williams, Keeda-Marie Hardisty, Paul Scorer and Robert Thatcher are employees of Oncologica UK Ltd. Maurizio Scaltriti is an employee of AstraZeneca. The other authors have nothing to declare. Supplementary Files Supplementarymaterialsdef.docx Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2024 Read the published version in The Oncologist → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":366798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePD-L1 status according to tumor groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend. \u003c/strong\u003e+: positive; -: negative; BC: breast cancer; CNS: central nervous system primary tumors; GI: gastrointestinal; GU: genitourinary (including prostate); Gyneco: gynecological malignancies; H\u0026amp;N: head and neck; HCC: hepatocellular carcinoma; PA: pancreatic adenocarcinoma; BT: biliary tract; Skin: includes melanoma and non-melanoma skin cancers. External circle’s % are referred to the proportion of PD-L1+ and – patients within each tumor type/group. Internal circle’s % are referred to the frequency of a specific tumor type/group with respect to the overall patients cohort.\u003c/p\u003e","description":"","filename":"image1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3949285/v1/675e5e39e467badc583a6342.jpg"},{"id":51189795,"identity":"a3b898b6-a5a5-4521-9952-c5f89432244f","added_by":"auto","created_at":"2024-02-15 16:51:58","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":375914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMutational landscape\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend. \u003c/strong\u003eTKR: tyrosine kinase receptor. In the central pie plot, OTHERS includes all mutant genes with a prevalence \u0026lt;0.8% and values are rounded.\u003c/p\u003e","description":"","filename":"image2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3949285/v1/242a57e6c72a86e205c55c82.jpg"},{"id":51189798,"identity":"624496e8-e61b-4c45-b592-628799a90b56","added_by":"auto","created_at":"2024-02-15 16:51:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":218088,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eActionability of detected mutations according to the ESCAT scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLegend. \u003c/strong\u003eA: Global proportion of different ESCAT tiers in the mutant cohort. Here, the best ESCAT tier for each single mutation detected was considered, independently of the tumor where it was detected. B: Prevalence of ESCAT I-II and III-V or X mutations according to tumor. ESMO: European Society for Medical Oncology; ESCAT: ESMO Scale for Clinical Actionability of Molecular Targets; CNS: central nervous system primary tumors; H\u0026amp;N: head and neck; HCC: hepatocellular carcinoma; NSCLC: non-small cell lung cancer; SCLC: small cell lung cancer; CUP: cancer of unknown primary.\u003c/p\u003e","description":"","filename":"image3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3949285/v1/95cfb9665ab7e13b999db160.jpg"},{"id":65196466,"identity":"3cf5d2a5-b223-44f2-b738-d4b10a875b60","added_by":"auto","created_at":"2024-09-24 15:28:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1896829,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3949285/v1/a2d3d27c-a649-4ceb-b713-a5e34ac72358.pdf"},{"id":51189796,"identity":"184c3201-94c7-49b1-a095-d3d896a03ca7","added_by":"auto","created_at":"2024-02-15 16:51:58","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":49797,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterialsdef.docx","url":"https://assets-eu.researchsquare.com/files/rs-3949285/v1/7f40cd01af0197f564465010.docx"}],"financialInterests":"Competing interest reported. Francesco Schettini reports honoraria from Novartis, Gilead and Daiichy-Sankyo for educational events/materials and travel expenses from Novartis, Gilead and Daiichy-Sankyo. Daniele Generali declares personal fees for educational events by Novartis, Lilly, Pfizer, Daiichy-Sankyo, Roche; research funds from Astrazeneca, Novartis and LILT. Marco Loddo, Gareth H Williams, Keeda-Marie Hardisty, Paul Scorer and Robert Thatcher are employees of Oncologica UK Ltd. Maurizio Scaltriti is an employee of AstraZeneca.\nThe other authors have nothing to declare.","formattedTitle":"Next-Generation Sequencing-Based Evaluation of the Actionable Mutational Landscape in Solid Tumors: the “MOZART” Prospective Observational Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWhen chemotherapy was first introduced for the treatment of solid tumors, the new and unexpected survival benefits observed prioritized the need to develop less toxic and more targeted therapeutic approaches\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Subsequently, improved screening programs, novel systemic treatments, refinement of surgical and radiotherapeutic techniques, along with more integrated multidisciplinary therapeutic approaches, have led to a visible increase in patients\u0026rsquo; survival in the last 3 decades, despite geographical disparities and differences according to cancer type\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This has led the scientific community to pursue the identification of increasingly personalized treatments to maximize therapeutic efficacy while sparing patients with unnecessary toxic treatments. In this perspective, a recent exponential increase in both tumor molecular profiling and discovery of effective molecularly targeted agents has provided new therapeutic opportunities for patients with advanced cancers harboring specific somatic and/or germline mutations\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Examples are represented by PARP inhibitors (PARPi) in germline \u003cem\u003eBRCA\u003c/em\u003e1/2-mutant ovarian, breast, prostate or pancreatic cancer\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, the PI3K-inhibitor (PI3Ki) alpelisib for \u003cem\u003ePIK3CA\u003c/em\u003e-mutant hormone receptor-positive (HR+)/HER2-negative breast cancer (BC), vemurafenib for \u003cem\u003eBRAF\u003c/em\u003e-mutant melanoma\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e or the tyrosine kinase inhibitors (TKI) entrectinib and larotrectinib, for solid tumors carrying NTRK fusions\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe identification of the most appropriate targeted therapies is challenging as a consequence of the limited coverage of most molecular profiling tests used in routine clinical practice and the frequent lack of direct evidence-based linkage with therapy\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These technical limitations translate into the loss of potential therapeutic opportunities for many patients. At our center, we have addressed this challenge by performing precision oncology genomic testing utilizing a comprehensive precision oncology next generation sequencing (NGS) assay\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, to identify the prevalence of potentially actionable mutations in a large cohort of patients with heavily-pretreated metastatic solid tumors, where therapeutic options are usually limited. Here we report the analysis of test trending data from this large screening programme to investigate the prevalence of actionable mutations directed at either the prescription of specific salvage targeted treatments (off-label or in indication) or alternatively to recruit patients to the most appropriate clinical trials. The NGS genomic data was also analyzed in combination with a PD-L1 companion diagnostic (CDx) immunohistochemistry (IHC) test, in order to assess potential correlations between clinically actionable tumor mutations and PD-L1 expression, a biomarker of response to anti-PD1/PD-L1 immune-checkpoint inhibitors (ICI)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and objectives\u003c/h2\u003e \u003cp\u003eThis was a prospective observational trial, named \u0026ldquo;the MOZART Trial\u0026rdquo;, that recruited adult patients with advanced solid tumors treated at the Oncology Unit of the Cremona Hospital (Italy) between 01/2019 and 03/2021. Patients had to be already pretreated with all available therapies for their metastatic disease and present with at least one accessible metastatic lesion to perform a biopsy. If not, archived formalin-fixed paraffin-embedded (FFPE) tumor tissue from the latest biopsy received was used (\u003cb\u003eSupplementary methods\u003c/b\u003e). The NGS and PD-L1 assays were applied to the tumor biopsies as described below. The primary objective of the present observational study, was to assess the prevalence of actionable molecular alterations in solid tumors in later treatment lines. Secondary objectives were to detect the levels of PD-L1 and potential association with specific actionable mutations. To guide therapeutic decision-making was not in the purpose of the present study. However, treating physicians were informed of the test results. If patients were deemed to be sufficiently fit to receive a further treatment line, physicians could then ask permission to their Institution for off-label prescription or compassionate drug use, unless an on-label matched mutation-treatment was available. If possible, patients were recruited in clinical trials, as well. PD-L1 assessment for immunotherapy prescription was repeated with an approved PD-L1 CDx assay whenever required, according to tumor type, treatment line and regulatory approval in Italy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eComprehensive NGS genomic profiling\u003c/h2\u003e \u003cp\u003eNGS genomic profiling including DNA extraction, library preparation, sequencing and data analysis was centrally performed on FFPE tumor tissue. The Oncofocus\u0026reg; assay (Oncologica\u0026reg; UK Ltd, Chesterford Research Park, Cambridge, UK), specifically adopted for the present molecular screening, is validated for clinical use and accredited by CLIA (ID 99D2170813) and by UKAS (9376) in compliance with ISO15189:2012 and following the guidelines published by the Association for Molecular Pathology and College of American Pathologists and IQN-Path ASBL\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. It is a NGS-based platform designed to detect druggable mutations, according to Food and Drug Administration (FDA) and European Medicine Agency (EMA) approvals, European Society for Medical Oncology (ESMO) and National Comprehensive Cancer Network (NCCN) guideline references or ongoing clinical trials worldwide and is updated every 12 weeks\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. It targets 505 genes and detects actionable genetic variants linked to 700 anti-cancer targeted therapies or combinations. For the purpose of this study, all genomic alterations detected were manually classified by using the ESMO Scale for Clinical Actionability of Molecular Targets (ESCAT), according to the latest guidelines and evidence found\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary references\u003c/b\u003e). DNA and RNA extraction, library preparation and sequencing, quality control metrics and data analysis methods are reported as \u003cb\u003eSupplementary methods.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry for PD-L1 expression and tumor mutational burden assessment\u003c/h2\u003e \u003cp\u003eThe assessment of PD-L1 immunostaining was performed by a qualified histopathologist at Oncologica\u0026reg; (G.W.), in accordance with PD-L1 clinical reporting guidelines\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, to quantify the proportion of tumor cells expressing PD-L1 (tumor proportion score) and the area occupied by tumor infiltrating PD-L1 positive immune cells in FFPE samples. According to the tumor proportional score (TPS), all cases were subdivided in PD-L1 negative (TPS\u0026thinsp;\u0026lt;\u0026thinsp;1%), positive-low (TPS\u0026thinsp;\u0026le;\u0026thinsp;1%\u0026le;TPS\u0026thinsp;\u0026le;\u0026thinsp;49%) and positive-high (TPS\u0026thinsp;\u0026ge;\u0026thinsp;50%)\u003csup\u003e14,15\u003c/sup\u003e. The rabbit monoclonal antibody clone E1L3N used for PD-L1 assessment is not licensed and approved for use in clinical testing to direct the use of PD1/PD-L1 therapies (\u003cb\u003eSupplementary methods\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTumor mutational burden (TMB) is usually defined as the number of somatic mutations per megabase (Mb) of interrogated genomic sequence and is a potential biomarker of response to ICI\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The Oncofocus assay covers\u0026thinsp;~\u0026thinsp;1.2 Mb over 505 genes, complying with the evidence-based suggestion that gene panels of at least 1 Mb are needed for TMB measurement\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Ten mutations/Mb was the cut-off to define high vs. low TMB cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics, Chi-square, Kruskal-Wallis or Wilcoxon rank-sum test with continuity correction were used where appropriate. Univariate logistic regressions were performed to assess associations between mutational or PD-L1 status and variables of interest. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. For all analyses Microsoft Excel 2019 vers.16.50 and R vers.3.6.1 for MacOS X were used.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003ePopulation characteristics\u003c/h2\u003e\n\u003cp\u003eOverall, 304 patients were included in this study. Median age was 56.3 years (interquartile range [IQR]: 59.0\u0026ndash;68.4) and patients were predominantly female (59.5%). All patients were sufficiently fit (ECOG 0\u0026ndash;2) to be treated with anticancer agents and were candidates to receive a salvage therapy or best supportive care (BSC) in the absence of suitable late-line treatments.\u003c/p\u003e\n\u003cp\u003eA total of 237 tumors (78%) harbored potentially actionable mutations detected with the NGS assay platform. BC was the most common tumor type, representing 25.3% of the entire cohort, followed by colorectal cancer (CRC) (12.5%), ovarian cancer (7.9%), prostate cancer (6.9%), primary malignancies of the central nervous system (6.6%) and non small cell lung cancer (NSCLC) (5.3%). With a prevalence\u0026thinsp;\u0026lt;\u0026thinsp;5% but \u0026gt;\u0026thinsp;2%, there were patients affected by bladder, pancreatic, kidney, biliary tract, cervical/vulvovaginal cancer, head and neck (H\u0026amp;N) tumors, soft-tissues sarcomas from several origins (abdominal, H\u0026amp;N, lung) and gastric cancer. Tumors with a prevalence\u0026thinsp;\u0026le;\u0026thinsp;2% in our cohort were small cell lung cancer (SCLC), endometrial, liver, non-melanoma skin cancers, melanoma, mesothelioma, cancer of unknown primary (CUP), neuroendocrine tumors (NET) from distinct origins (pancreas, lung, intestine, unknown), thymic carcinoma and penile cancer. The main population characteristics are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePatients characteristics\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCHARACTERISTICS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOVERALL POPULATION\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e304\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePatients' Age\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIQR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.0-68.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCancer Type*\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCarcinomas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e268\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNon-carcinomas (excluding hematologic malignancies)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMutational Status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMutant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e237\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWild-Type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMutation per Tumor\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIQR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRange\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026ndash;8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003ePD-L1 Status\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMedian %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIQR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNegative (TPS\u0026thinsp;\u0026lt;\u0026thinsp;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive-low (1%\u0026le;TPS\u0026thinsp;\u0026lt;\u0026thinsp;49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e173\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePositive-high(TPS\u0026thinsp;\u0026ge;\u0026thinsp;50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eOverall\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e280\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e92.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eLegend.\u003c/strong\u003e TPS: tumor proportion score; IQR: interquartile range; CNS: central nervous system; NSCLC: non-small cell lung cancer; SCLC: small cell lung cancer; CUP: cancer of unknown primary, TPS: tumor proportion score. *: cancer types and frequency are detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eTumors were positive for PD-L1 according to TPS in 68.9% cases, with high levels (TPS\u0026thinsp;\u0026ge;\u0026thinsp;50%) observed in 7.1%. In 24 cases, the material to analyze both PD-L1 and tumor mutational status was insufficient. In these cases, the NGS assay was prioritized. The distribution of PD-L1-positive (+) vs. PD-L1-negative (-) tumors across cancer types did not reach the statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eStill, two macro groups with significant difference in PD-L1 status could be ultimately identified (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Namely, a group including BC, CNS, genitourinary/prostate (GU) and Gynecological malignancies (80% PD-L1+) and a group including gastrointestinal (GI), liver, pancreatic, biliary tract, pulmonary, pleural, H\u0026amp;N, skin and rare tumors (61% PD-L1+).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eMolecular results\u003c/h2\u003e\n\u003cp\u003eOverall, 236 tumors harbored at least one mutation in genes screened by the Oncofocus\u0026reg; panel (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDetailed cancer type incidence in the study cohort\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eCHARACTERISTICS\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOVERALL POPULATION\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMUTANT POPULATION\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e%*\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e%\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e304\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e236\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e77.6\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eCancer Type\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBreast\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e25.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eColorectal, anal and small intestine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e94.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOvarian\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProstate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e33.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCNS Primary\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNSCLC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e87.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBladder/ureter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e88.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePancreas\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e86.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKidney\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGallbladder and biliary tract\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCervix/Vagina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e57.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSarcoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHead and Neck\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e71.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGastric\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e85.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSCLC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLiver\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e66.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMesothelioma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e75.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUterine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMelanoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSkin non-melanoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCUP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e50.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeuroendocrine\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThymic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePenis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eLegend.\u003c/strong\u003e CNS: central nervous system; NSCLC: non-small cell lung cancer; SCLC: small cell lung cancer; CUP: cancer of unknown primary. *: The proportions in the overall study cohort are referred to the frequency of each specific cancer type with respect to the total population of 304 patients. #: The proportions in the mutant study cohort are referred to the total of each cancer type.\u003c/p\u003e\n\u003cp\u003eA total of 549 mutations were detected (full list available as \u003cstrong\u003eSupplementary Table\u0026nbsp;1\u003c/strong\u003e), along with 1 case of high TMB (TMB-H) in a NSCLC. In multiple cases the same mutation was detected in different tumors, leading to a total of 272 distinct mutations. The median number of mutant genes \u003cem\u003eper\u003c/em\u003e tumor was of 2.0 (IQR: 1.0\u0026ndash;3.0, minimum-maximum range [min-max]: 0.0\u0026ndash;8.0). The median mutational frequency rate was 77.8% (IQR: 66.7% \u0026minus;\u0026thinsp;92.1%; min-max: 0.0% \u0026minus;\u0026thinsp;100.0%). The tumors in the lower quartile (Q1) of mutational frequency were ovarian, gallbladder/biliary tract, liver, cervix/vaginal, non-melanoma skin cancers, sarcomas, CUPs, penis and prostate cancer. In the following quartile (Q2) were included mesothelioma, kidney and H\u0026amp;N cancers, whilst in the subsequent quartile (Q3) BC, NSCLC, CNS primary tumors, bladder/ureter, pancreatic and gastric cancer were included. The upper quartile (Q4) included colorectal/anal/small intestine, uterine, thymic, neuroendocrine cancers, SCLC and melanomas. As expectable, the difference in mutant vs. non-mutant tumors for each quartile group was significantly higher as the quartile increased (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with an increasing proportion \u003cem\u003eper\u003c/em\u003e quartile of cases harbouring at least one mutation in covered genes (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eProportion of mutant tumors \u003cem\u003eper\u003c/em\u003e mutational frequency quartiles and distribution of actionable mutations \u003cem\u003eper\u003c/em\u003e quartile\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMUTATIONAL STATUS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQ1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQ2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQ3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eQ4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eMutant tumors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNon-mutant tumors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal number of patients\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eESCAT I-II mutations\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eESCAT\u0026thinsp;\u0026ge;\u0026thinsp;III mutations\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e160\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal number of mutations\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e118\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eLegend.\u003c/strong\u003e Q: quartile; Q1: \u0026le;66.7% mutant tumors \u003cem\u003eper\u003c/em\u003e cancer type; Q2: \u0026gt;66.7% and \u0026le;\u0026thinsp;77.8% mutant tumors \u003cem\u003eper\u003c/em\u003e cancer type; Q3: \u0026gt;77.8% and \u0026le;\u0026thinsp;91.2% mutant tumors \u003cem\u003eper\u003c/em\u003e cancer type; Q4: \u0026gt;91.2% and \u0026le;\u0026thinsp;100.0% mutant tumors \u003cem\u003eper\u003c/em\u003e cancer type.\u003c/p\u003e\n\u003cp\u003eConsistently, a statistically significant difference in the median number of mutations according to tumor type was observed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with CNS tumors showing a median of 3.0 (IQR: 1.75\u0026ndash;3.25), followed by GI tumors presenting with a median of 2.0 (IQR: 2.0\u0026ndash;3.0), breast tumors showing a median of 2.0 altered genes \u003cem\u003eper\u003c/em\u003e tumor (IQR: 1.0\u0026ndash;3.0), liver, pancreatic and biliary tract cancers exhibiting a median of 2.0 (IQR: 1.0\u0026ndash;2.0). No difference in mutation number according to sex and age (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.279\u003c/em\u003e and \u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.098\u003c/em\u003e respectively) was observed.\u003c/p\u003e\n\u003cp\u003eThe pathways most frequently involved were related to DNA damage repair (14%), the PI3K/AKT/mTOR (14%) and the RAS/RAF/MAPK (12%) pathways. Moreover, many genes involved in cell cycle control and gene expression regulation were found mutated (32% cases). In fact, the most frequently altered gene was \u003cem\u003eTP53\u003c/em\u003e, mutated in 20.2% patients, followed by \u003cem\u003eBRCA2\u003c/em\u003e (7.2%), \u003cem\u003ePIK3CA\u003c/em\u003e (7%), \u003cem\u003eKRAS (\u003c/em\u003e6.6%), \u003cem\u003eARID1A\u003c/em\u003e (3.5%), \u003cem\u003eNF1\u003c/em\u003e (3.5%) and \u003cem\u003eATM\u003c/em\u003e (3.1%) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAlterations in cancer-related genes were derived from a range of different mechanisms, including substitution, deletion, amplification, fusion, insertion, chromosomic aberration, deletion/insertion, exon skipping and pathogenetic gene variants. Substitutions were the predominant mutation type, detected in 49.3% of the samples, followed by deletions (16.8%), amplifications (9.5%), gene fusions (4.7%), insertions (2.6%), chromosomic aberrations (1.1%) and other types of mutations with lower frequency but collectively representing 14.6% of cases (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eRegarding PD-L1, we did not detect any association between PD-L1 status (positive vs. negative) and tumor mutational status (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.454)\u003c/em\u003e or a difference in mutation number according to PD-L1 category (\u003cem\u003ep\u0026thinsp;=\u0026thinsp;0.717\u003c/em\u003e). PD-L1 TPS score as continuous variable was also not associated to mutational status (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.840).\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n\u003ch2\u003eActionability of the detected mutations\u003c/h2\u003e\n\u003cp\u003eWhen considering the ESCAT scale to evaluate the clinical actionability of our cohort\u0026rsquo;s mutational landscape, we observed that among the total 273 different alterations identified (including TMB-H), only 19.4% were considerable as Tier I for at least one cancer type, while 16.1% were ESCAT Tier II, 6.6% ESCAT Tier III and the rest was Tier IV-V or X (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWhen we matched the genomic alteration with the respective ESCAT Tier according to the tumor where the mutation was actually detected, we observed that mutant non-breast tumors were significantly less likely to be associated with ESCAT Tier I-II mutations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In fact, actionable ESCAT Tier I-II mutations in relevant proportion (\u0026gt;\u0026thinsp;10%) were detected only for BC (77.9%), melanoma (66.7%), prostate cancer (44.4%) and NSCLC (11.8%). ESCAT Tier I-II mutations were found also in H\u0026amp;N (6.7%), bladder (6.7%), gastric (5.9%), CNS (5.7%) ovarian (4.8%) and CRC (3.6%) in \u0026lt;\u0026thinsp;7% of cases. In the remaining tumor types, only ESCAT Tier III-V or X mutations were found (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Consistently, when subdividing cancer types according to mutational frequency, a higher proportion of mutations was not necessarily a synonym of actionability, since the vast majority of actionable ESCAT-I/II mutations were found in the Q3 group (30.1%; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) rather than Q4. In fact, the proportion of actionable mutations \u003cem\u003eper\u003c/em\u003e group was low and similar between Q1, Q2 and Q4 (5.9, 3.2, 4.2%, respectively) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The Q3 group included BC, NSCLC, CNS primary tumors, bladder/ureter, pancreatic and gastric cancer; the higher actionability was driven by BC and NSCLC (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Finally, no association was found between PD-L1 status and the presence of ESCAT tier I-II mutations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.148).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eExamples of matched treatments\u003c/h2\u003e\n\u003cp\u003eWe could only retrospectively retrieve data on clinical practice therapeutic decision-making and outcomes from patients\u0026rsquo; charts. Overall, 62 out of 304 (20.4%) patients were treated according to the genomic assay or PD-L1 test result through an on-label, off-label or compassionate use prescribing procedure, or in the context of a clinical trial. When required, PD-L1 positivity was confirmed with an approved CDx. Twenty-three (37.1%) patients obtained the most relevant clinical benefit, achieving partial or complete responses as their best response according to clinical judgement, with a median progression-free interval (PFI) of 32.0 months (95% confidence interval [CI]: 18.5 months \u0026ndash; 84.0 months). These patients were affected by either BC, endometrial cancer, mesothelioma or glioblastoma. In these tumors, the test identified from one to two mutations or PD-L1 positivity. The other 39 tested cancers (bladder, gallbladder, endometrial, lung, prostate, parathyroid, ovary, melanoma, sarcoma) obtained either disease progression or stability as their best response, with a median PFI of 16.0 months (95%CI: 6.0 months \u0026ndash; 23.0 months). In these tumors, the test detected a PD-L1 positivity and/or several mutations, suggesting the presence of different \u0026ldquo;passenger\u0026rdquo; genomic alterations, likely not directly involved into cancer progression and responsiveness to specific target therapies.\u003c/p\u003e\n\u003cp\u003eTwo patients with different tumors harboring the same mutation and both treated with off-label abemaciclib gave consent to report their non-aggregated clinical outcomes. A first case, was a chemotherapy-refractory, \u003cem\u003eCDKN2B\u003c/em\u003e-deleted metastatic parotid tumors, that received abemaciclib as fourth-line therapy. In this setting the inhibition resulted in a PFI of 18.5 months and improved quality of life. The second case was a chemotherapy-refractory, \u003cem\u003eCDKN2B\u003c/em\u003e-deleted metastatic endometrial adenocarcinoma treated with abemaciclib as third-line therapy. In this setting the inhibition resulted in a poor PFI of 2.5 months, with mild improvement in quality of life.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur MOZART prospective observational study performed at the Cremona Hospital has demonstrated that precision oncology molecular profiling in advanced solid malignancies is of potential clinical value. The overall mutation detection rate was 78% with clinically actionable mutations of ESCAT Tier I-II detected in 35.5% cases with an additional 6.6% falling within the ESCAT Tier III category. BC, prostate cancer, melanoma and NSCLC were the most frequently mutated tumors with clinically actionable mutations of ESCAT Tier I-II, followed by H\u0026amp;N, bladder, gastric, CNS, ovarian and CRC. No associations were observed between PD-L1 status, age, sex and tumor mutational status. GI and biliary tract tumors were the cancer types most frequently associated with PD-L1 positivity.\u003c/p\u003e\n\u003cp\u003eThe genetic variants described in our study belong to some of the most common key cancer regulatory networks in solid tumors, such as the RAS/RAF/MAPK and PI3K/AKT/mTOR signaling pathways, the DNA damage repair (DDR) pathways and cell cycle checkpoints. Notably, many approved or experimental targeted agents are precisely directed against these targets, for example the PI3K inhibitors in BC, mTOR inhibitors in breast and kidney cancer, AKT inhibitors in BC, BRAF and MEK inhibitors in melanoma or PARP inhibitors in several solid tumors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. However, these drugs are costly, present with several side effects and are often administered without identification of the altered pathway of interest (e.g. everolimus or capivasertib). This relates to the fact that the regulatory agencies have granted approval for these targeted agents without requirement for an accompanying CDx. Taking into account that their correct positioning in the therapeutic algorithms frequently present many uncertainties, a broader implementation of molecular testing would greatly improve selection of patients for these targeted agents and establish more personalized therapeutic algorithms when such uncertainty exists.\u003c/p\u003e\n\u003cp\u003eThe rate of patients with ESCAT levels I-III identified in this study was comparable with other published screening programs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The ESCAT framework was promoted by a group of experts who reached a consensus on the criteria to define the clinical actionability of somatic mutations in solid tumors culminating in the establishment of the ESMO-ESCAT scale\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In this way, clinicians now have the opportunity to become familiar with the type of genomic variants reported by genomic tests and their relevance for treatment. This is especially important following the results from the SHIVA and the SAFIR01 trials which both showed no improvement in progression-free survival (PFS)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and disappointing overall response rates (ORR) of only 9%\u003csup\u003e28\u003c/sup\u003e in pretreated patients with advanced solid tumors, when the drug-genomic alteration match was not guided by sufficient evidence of clinical activity or efficacy. In fact, in our cohort, only 20% patients ultimately received a mutation-matched treatment, with less than half experiencing an objective response and a prolonged clinical benefit. From this perspective it is critical to interpret mutations in a tumor type-specific manner. The most relevant example of this kind is the \u003cem\u003eBRAF V600E\u003c/em\u003e mutation, which is considered Tier I for melanoma, since effective B-Raf inhibitors like dabrafenib and vemurafenib are already approved in the clinic as standard of care treatments\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Conversely, it is considered Tier III for CRC, due to the limited activity of vemurafenib in this particular tumor type\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, and Tier II in cholangiocarcinoma, following positive results for the combination of dabrafenib and trametinib in the ROAR trial\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. An exception is represented by specific rare driver mutations that can be targeted by molecular inhibitors approved in tumor-agnostic fashion, like \u003cem\u003eNTRK\u003c/em\u003e fusions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eRET\u003c/em\u003e fusions\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and alterations in genes involved in the DNA mismatch repair mechanism\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eImportantly, a pooled analysis from the SAFIR02-BREAST and SAFIR-PI3K trials showed a significant PFS benefit (60% reduction in the risk of progression and death, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) only in the presence of ESCAT Tier I-II alterations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. These results highlight the importance of avoiding an excess of off-label prescription of target drugs, especially if only preclinical evidence of activity exist or only activity/efficacy in other cancer types has been proved. At the same time, targeted treatments with a good preclinical rationale should not be completely discarded in cases with very limited therapeutic options. The two clinical experiences reported within our genomic profiling programme support both the concept that genomic testing for off-label drug prescription should be adopted with caution, as well as the concept that such a genomic profiling programme might represent an opportunity to potentially give access to valuable treatments when the alternatives are scarce. In both cases, abemaciclib, a CDK4/6-inhibitor, was administered based on preclinical and phase I evidence supporting \u003cem\u003eCDKN2B\u003c/em\u003e deletions as potential biomarker of efficacy to CDK4/6 pharmacologic inhibition\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOverall, we strongly believe that the implementation of molecular tumor boards (MTB) for the correct interpretation of genomic testing results and their correct implementation in clinical practice, can optimize on-label and off-label target therapy prescriptions, as well as possible recruitment in clinical trials\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In fact, MTB have already been introduced in many institutions, resulting in a significant optimization of target therapy prescriptions, as highlighted in several reports, especially for rare and/or complex tumor mutational profiles\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Particularly interesting, in this perspective, is also the Molecular Tumor Board Portal initiative of the Cancer Core Europe consortium, based on a unified legal, scientific and technological platform to share and harness NGS data\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. The ultimate goal is to automate the interpretation and reporting of complex molecular testing results, adopt a consistent expert-agreed process to systematically link tumor molecular profiles with clinical actions and reduce the need for time-consuming manual procedures, potentially prone to errors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Also, very promising seems to be the implementation of artificial intelligence (AI)-based learning programs to uniform treatment recommendations among different MTB, or directly provide treatment recommendations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Further research is however needed to deliver more solid evidences in this regard.\u003c/p\u003e\n\u003cp\u003eIn our study, a high percentage of tumor samples harbored mutations involved in DNA repair mechanisms including \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eBRCA1/2\u003c/em\u003e and \u003cem\u003eATM\u003c/em\u003e. Many targeted agents directed towards these pathways are currently investigated in clinical trials or have already entered frontline clinical practice such as PARP inhibitors\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In some cases, like PARP inhibitors in BC, approval has been granted in case of germline mutant alterations\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Approximately 5\u0026ndash;10% of all solid tumors are hereditary and germline pathogenetic variants of these genes are responsible for many of currently known cancer hereditary syndromes\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Although somatic genomic testing should not be performed with this purpose, our data suggest that for specific cancers and in selected cases where the oncologic family history is not available, unclear or not particularly suspicious or when appropriate genetic counselling cannot be offered, genomic profiling might also help identifying carriers of germline cancer-associated variants. This is important, considering the implications on treatment for the patient and on cancer prevention for family members\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. However, suspected inheritable alterations should be further investigated with appropriate testing.\u003c/p\u003e\n\u003cp\u003eOur study has shown that a wide number of structural abnormalities are associated with genomic instability. The majority of mutations identified are considered as nucleotide instabilities (NIN), a type of DNA alteration characterized by an increased frequency of substitutions, deletions, and insertions of one or few nucleotides\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Following NIN, chromosomal instability (CIN) was the second most prevalent form of genomic instability identified. These findings are in line with previous studies\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Since CIN is correlated to intrinsic multidrug resistance and poor prognosis, its detection \u003cem\u003eper se\u003c/em\u003e might be clinically relevant and represent an additional information for the personalization of cancer care\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIt is still unclear how often genomic profiling should be repeated during the disease course because of potential genomic variability between primary and metastatic tumor, as well as between different metastatic sites\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. For example, within the AURORA molecular screening program an extensive profiling of BC paired primary tumors and metastatic sample was carried out, showing an overall increase in clonality in metastatic samples\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Nevertheless, when Van de Haar \u003cem\u003eet al\u003c/em\u003e evaluated the differences in the actionable genomic landscape between biopsy pairs longitudinally collected over the treatment course in patients with different metastatic cancers\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, a full concordance in standard-of-care genomic biomarkers and similar ESCAT tier II mutations\u0026rsquo; rate between the first and second biopsy was observed in 99% of the pairs\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. These results suggest that, while differences might be likely observed between the primary and the metastatic disease in terms of mutational profile, for the majority of metastatic cancer patients, there is a limited evolution of the actionable genome over time. Thus, a single NGS-based analysis on a metastatic sample might be both sufficient and effective to guide treatments or to evaluate a clinical trial enrollment, avoiding the need to perform multiple biopsies over time.\u003c/p\u003e\n\u003cp\u003eFinally, PD-L1 is now used as a major predictive biomarker of response to ICI\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Here we aimed at evaluating its association with genomic alterations in solid tumors to speculate on a potential role beyond immunotherapy. However we did not identify any linkages between PD-L1 expression and actionable mutations status in our study cohort.\u003c/p\u003e\n\u003cp\u003eThe main limitations of this study relate to the fact that this was performed in a single centre setting, accompanied by the lack of data regarding most mutation-matched treatments and patients survival outcomes, which prevented us from comprehensively assessing the practical impact of genomic profiling.\u003c/p\u003e\n\u003cp\u003eIn summary this study highlights the clinical value of molecular profiling in metastatic solid tumors using NGS-based panels. We observed a high overall mutation detection rate, with clinically actionable mutations found in a significant proportion of cases and especially high in BC, prostate cancer, melanoma, and NSCLC. Hence, implementing molecular testing can aid in selecting patients for targeted therapies, improve treatment algorithms in situations of uncertainty and facilitate clinical trial recruitment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDaniele Generali and Francesco Schettini conceived the manuscript. Francesco Schettini carried out the statistical analyses. Francesco Schettini, Daniele Generali and Marianna Sirico wrote the first manuscript draft. All authors except Francesco Schettini and Pablo Rivera were involved in patients\u0026rsquo; management or testing. All authors had access to study results, revised and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval, consent to participate and consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Local Ethics Committee Val Padana (IRB n.32219) on December 21, 2018. All patients provided written informed consent to participate and consent for publication of the anonymized research results. The study was performed in accordance with the Declaration of Helsinki, Good Clinical Practice (GCP) and local legislation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnonymized data are available upon reasonable request from the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrancesco Schettini reports honoraria from Novartis, Gilead and Daiichy-Sankyo for educational events/materials and travel expenses from Novartis, Gilead and Daiichy-Sankyo. Daniele Generali declares personal fees for educational events by Novartis, Lilly, Pfizer, Daiichy-Sankyo, Roche; research funds from Astrazeneca, Novartis and LILT. Marco Loddo, Gareth H Williams, Keeda-Marie Hardisty, Paul Scorer and Robert Thatcher are employees of Oncologica UK Ltd. Maurizio Scaltriti is an employee of AstraZeneca.\u003c/p\u003e\n\u003cp\u003eThe other authors have nothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by Mednote, spin-off of the University of Trieste.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Francesco Schettini is supported by a Rio Hortega clinical scientist contract from the Instituto de Salud Carlos III (ISCIII). Opinions and hypotheses generated are solely of the article\u0026rsquo;s authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDeVita VT, Chu E. A history of cancer chemotherapy. \u003cem\u003eCancer Res\u003c/em\u003e. November 1, 2008;68(21):8643\u0026ndash;8653. \u003c/li\u003e\n\u003cli\u003eAllemani C, Matsuda T, Di Carlo V, Harewood R, Matz M, Nik\u0026scaron;ić M, et al. 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March 1, 2011;71(5):1858\u0026ndash;1870.\u003c/li\u003e\n\u003cli\u003eA W, R H, I T. Association between chromosomal instability and prognosis in colorectal cancer: a meta-analysis. \u003cem\u003eGut\u003c/em\u003e. July 2008;57(7):941\u0026ndash;950.\u003c/li\u003e\n\u003cli\u003eFancello L, Gandini S, Pelicci PG, Mazzarella L. Tumor mutational burden quantification from targeted gene panels: major advancements and challenges.\u003c/li\u003e\n\u003cli\u003eAftimos P, Oliveira M, Irrthum A, Fumagalli D, Sotiriou C, Gal-Yam EN, et al. Genomic and Transcriptomic Analyses of Breast Cancer Primaries and Matched Metastases in AURORA, the Breast International Group (BIG) Molecular Screening Initiative. \u003cem\u003eCancer Discov\u003c/em\u003e. November 2021;11(11):2796\u0026ndash;2811.\u003c/li\u003e\n\u003cli\u003evan de Haar J, Hoes LR, Roepman P, Lolkema MP, Verheul HMW, Gelderblom H, et al. 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September 2021;27(9):1553\u0026ndash;1563.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"solid tumors, mutation, ESCAT, clinical actionability, molecular profiling, metastatic","lastPublishedDoi":"10.21203/rs.3.rs-3949285/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3949285/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe identification of the most appropriate targeted therapies for advanced cancers is challenging. We performed a molecular profiling of metastatic solid tumors utilizing a comprehensive next-generation sequencing (NGS) assay to determine mutations\u0026rsquo; type, frequency and actionability and potential correlations with PD-L1 expression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e304 adult patients with heavily-pretreated metastatic cancers treated between 01/2019-03/2021 were recruited. The CLIA-/UKAS-accredit Oncofocus\u0026reg; assay targeting 505 genes was used on newly-obtained or archived biopsies. Chi-square, Kruskal-Wallis and Wilcoxon rank-sum test were used where appropriate. Results were significant for p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 237 tumors (78%) harbored actionable mutations. Tumors were positive for PD-L1 in 68.9% cases. The median number of mutant genes/tumor was of 2.0 (IQR: 1.0\u0026ndash;3.0). Only 34.5% were actionable ESCAT Tier I-II with different prevalence according to cancer type. The DNA damage repair (14%), the PI3K/AKT/mTOR (14%) and the RAS/RAF/MAPK (12%) pathways were the most frequently altered. No association was found between PD-L1, ESCAT, age, sex and tumor mutational status. Sixty-two patients underwent targeted treatment, with 37.1% obtaining objective responses.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eWe highlight the clinical value of molecular profiling in metastatic solid tumors using comprehensive NGS-based panels to improve treatment algorithms in situations of uncertainty and facilitate clinical trial recruitment.\u003c/p\u003e","manuscriptTitle":"Next-Generation Sequencing-Based Evaluation of the Actionable Mutational Landscape in Solid Tumors: the “MOZART” Prospective Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-15 16:51:54","doi":"10.21203/rs.3.rs-3949285/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cc32adaf-4443-4f5e-9cb4-7eb500116427","owner":[],"postedDate":"February 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-24T15:28:26+00:00","versionOfRecord":{"articleIdentity":"rs-3949285","link":"https://doi.org/10.1093/oncolo/oyae206","journal":{"identity":"the-oncologist","isVorOnly":true,"title":"The Oncologist"},"publishedOn":"2024-08-23 00:00:00","publishedOnDateReadable":"August 23rd, 2024"},"versionCreatedAt":"2024-02-15 16:51:54","video":"","vorDoi":"10.1093/oncolo/oyae206","vorDoiUrl":"https://doi.org/10.1093/oncolo/oyae206","workflowStages":[]},"version":"v1","identity":"rs-3949285","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3949285","identity":"rs-3949285","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}