Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Filter by Categories
Brief Commentary
Cardio Oncology with ACOS
Case Report
Case Series
Conference Review
Consensus Statement
Current Issue
Editorial
Erratum
Letter to Editor
Media and News
Molecular Insight Story
New Drug Update
News
Original Article
Position Paper
Response to the letter
Review Article
Short Communication
Generic selectors
Exact matches only
Search in title
Search in content
Post Type Selectors
Filter by Categories
Brief Commentary
Cardio Oncology with ACOS
Case Report
Case Series
Conference Review
Consensus Statement
Current Issue
Editorial
Erratum
Letter to Editor
Media and News
Molecular Insight Story
New Drug Update
News
Original Article
Position Paper
Response to the letter
Review Article
Short Communication
View/Download PDF

Translate this page into:

Review Article
8 (
3
); 81-88
doi:
10.25259/IJMIO_12_2023

Mini review: Molecular pathology of personalized medicine in cancer susceptibility syndromes

Department of Genetics and Molecular Medicine, Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Mumbai, Maharashtra, India
Department of Laboratory Medicine and Advanced Diagnostics, Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Mumbai, Maharashtra, India
Center of Cancer, Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Mumbai, Maharashtra, India

*Corresponding author: Amrit Kaur Kaler, Consultant, Department of Genetics and Molecular Medicine, Kokilaben Dhirubhai Ambani Hospital and Medical Research Institute, Maharashtra, Mumbai, India. amrit_kaler@yahoo.co.in

Licence
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

How to cite this article: Kaler AK, Bora NS, Kavyashree P, Nikam A, Rane S, Tiwarekar Y, et al. Mini review: Molecular pathology of personalized medicine in cancer susceptibility syndromes. Int J Mol Immuno Oncol 2023;8:81-8.

Abstract

In current times, medical oncology is increasingly incorporating cancer genetics and genetic testing into its practice. About 5–10% of all cancers are caused due to inherited genetic mutation that increases susceptibility to a particular malignancy. There is an increasing practice of incorporation of genetic testing and results with potential benefits that have been seen in current-day oncology practice. The American College of Medical Genetics and Genomics highly advises conducting clinical molecular genetic testing within a laboratory that has received CLIA approval with results accurately interpreted by molecular geneticists. The patient is highly recommended to talk to a genetic specialist to explain about the risk, document the family history, and also explain the limitations and outcomes of the genetic testing. Nonetheless, significant discussions and ambiguity persist regarding the optimal approach for providing genetic testing services. These include considerations such as which tests should be employed, which patients should undergo testing, the order and timing of the tests, who should administer them, and the appropriate course of action for follow-up.

Keywords

Molecular pathology
Personalized medicine
Cancer susceptibility syndromes
Solid tumors
Hematological malignancies

INTRODUCTION

Familial cancers can occur in multiple individuals within the same family but are not caused due to single gene mutation.[1,2] These type of cancers cluster within families but are not hereditary in nature and result from a combination of various factors, such as multiple genes, and lifestyle factors such as diet and exercise, which collectively increases the risk of developing cancers.[3] Hereditary cancers are caused due to germline mutations in specific genes that are inherited from either one or both parents and are associated with susceptibility to particular cancers. Mostly autosomal dominant in nature, meaning the probability of 50% passing on this mutation or change to the next generation.[4] An individual who tested positive on a hereditary cancer test has more than one pathogenic variant in 3.1% of cases.[5] While germline mutations occur in all the cells of the body, cancers that are non-hereditary or sporadic are caused due to genetic mutations in the tumor cells or tissues concerned. These mutations are known as somatic mutations and are not inherited by the next generation.

A few of the cancers follow autosomal recessive patterns of inheritance, such as MUTYH-associated polyposis (MAP). MAP is associated with germline mutations in both the copies of the MUTYH gene, one copy of each mutated allele inherited from the maternal and paternal sides. Patients developing colon cancer at an early age and having healthy parents and/ or the presence of 15–20 colonic adenomas are indications of the possibility of MAP in an individual.[6]

Hereditary basis of breast cancer was first described by Broca more than a century ago in 1866 when he reported breast cancer in multiple family members.[7] Warthin and Henry Lynch reported distinct patterns of early age onset and multiple primary tumors in the same individual with colon cancer.[8,9] Li and Fraumeni described the clinical characteristics of families with p53 germline mutations as Li-Fraumeni syndrome (LFS) in the subsequent years.[10] In 1971 Knudson propound the two-hit hypothesis in Retinoblastoma (RB), where he suggested that the patients who had bilateral RB carried an inherited gene mutation along with an acquired second mutation.[11] Later, Friend SH et al. also confirmed the same hypothesis by and reported a high incidence of 2nd non-ocular tumor which was believed to be caused by the same mutation.[12]

There have been more than 100 distinct syndromes found, and the majority are rare.[13] About 5–10% of all cancers are caused due to inherited genetic mutation that increases susceptibility to a particular malignancy.[4,14] Being familiar with the more-prevalent syndromes, such as hereditary breast and ovarian cancer, Li-Fraumeni, Lynch Syndrome (HPNCC), familial adenomatous polyposis (FAP), RB, multiple endocrine neoplasia, and Von Hippel-Lindau (VHL) can help health-care professionals recognize different signs and symptoms in a patient as potentially having a genetic component, allowing for appropriate diagnostic testing and referrals.[4]

There has been a tremendous advancement in next-generation sequencing (NGS) technologies in the last 30 years. The application of massively parallel sequencing in germline and somatic cancers has become clinically important. Germline testing assists in identifying the risk of inherited cancer in an individual and at-risk family members and benefit in risk-reducing measures and cancer surveillance.[14] While somatic testing helps in therapeutic options for targeted therapies and immunotherapies.[15] Although germline and somatic testing are carried out independently oftentimes in diagnostic laboratories, integrating both approaches to provide optimal care for individuals affected with diverse forms of cancer.[16-18]

PENETRANCE AND EXPRESSIVITY

Penetrance is a measurement of the proportion of individuals in a population who carry a particular pathogenic mutation and exhibit the disease phenotype. For example, Mutations in the RB1, APC, BRCA1 and 2, PTEN genes as mentioned in [Table 1]. But in some syndromes, the association between the gene and its expressivity is reduced, like incomplete penetrance shown in Wilms tumor.

Table 1: Few examples of significant cancer susceptibility syndromes, their patterns of inheritance, and penetrance.[19]
Cancer syndrome Gene Main tumor type Penetrance Patterns of inheritance
FAP APC Colorectal carcinoma 70–100% Autosomal dominant
Cowden’s syndrome PTEN Breast, endometrium, follicular thyroid tumor 90–95% Autosomal dominant
HBOC BRCA1 and BRCA2 Breast/ovary Up to 85% Autosomal dominant
LFS TP53 AML, sarcoma, adrenocortical Carcinoma 90–100% Autosomal dominant
Lynch syndrome/HNPCC MLH1, MSH2, MSH6, PMS1, PMS2 Colorectal, endometrium, brain 90% Autosomal dominant
RB RB Eye, bone 90% Autosomal dominant
Wilms’ tumor syndromes WT1 Nephroblastoma 30% incomplete Autosomal dominant
Gorlin syndrome/NBCC PTCH1 Basal cell carcinoma/medulloblastoma 90% Autosomal dominant
ATS ATM Lymphomas, leukemia 100% Autosomal recessive
FA FANCA, FANCB, FANCC, FANCD, FANCE, FANCF, FANCG, FANCL Acute myeloid leukemia 100% Autosomal recessive
BS BLM Wilms tumor, colorectal cancers, Leukemia 100% Autosomal recessive

FAP: Familial adenomatous polyposis, HBOC: Hereditary breast and ovarian cancer syndrome, LFS: Li-Fraumeni syndrome, HNPCC: Hereditary nonpolyposis colon cancer, NBCC: Nevoid basal cell carcinoma, ATS: Ataxia-telangiectasia syndrome, RB: Retinoblastoma, FA: Fanconi anemia, BS: Bloom syndrome

The degree to which a genotype manifests its phenotypic expression is measured by expressivity. Different levels of expression in different people may result from variations in the allelic makeup of the rest of the genome or from environmental influences. Thus, expressivity quantifies the degree to which a genotype is phenotypically expressed in individuals, as opposed to penetrance measurements that concentrate on whether or not a disease is expressed in a population.[19,20]

Understanding the penetrance and expressivity of cancer-predisposing genes is significant in understanding the complexity of hereditary cancers and improving genetic counseling for patients as well as family members.[19]

INDICATIONS FOR GERMLINE CANCER TESTING INCLUDES

  1. Breast cancer diagnosis in ≤50 years age: Triple negative subtype or Lobular Carcinoma; Male Bilateral/multiple primary, Ashkenazi Jewish Ancestry, Breast cancer and one additional tumor (LFS, ≥1 PJ polyp, Cowden syndrome).

  2. Colorectal cancer (CRC) diagnosed at age <50: Mismatch repair deficient; multiple primary synchronous or metachronous CRC; ≥10 adenomatous or >5 hamartomatous gastrointestinal polyps; association with other cancers – endometrial, LFS, Cowden syndrome criteria.

  3. All women are diagnosed with ovarian cancer whether it is a single case present in the patient or a first-degree relative. BRCA1 and BRCA2 pathogenic germline variants are detected in the vast majority of ovarian cancer patients, specifically with high-grade serous histology.

  4. Prostate cancer diagnosed at any age: Intraductal/ cribriform histology, Gleason score ≥7; metastatic, regional (node-positive) or very-high-risk localized prostate; Ashkenanzi Jewish ancestry.

  5. Pancreatic cancer diagnosed at any age: Intraductal papillary mucinous neoplasm histopathology.

  6. Patients diagnosed with renal cancer, having age of diagnosis <50; Bilateral or multifocal tumors; ≥1 close relative renal cell carcinoma (RCC) with clear cell, papillary type 1, papillary type 2, collecting duct, tubulopapillary and Birt-Hogg-Dubé (BHD)-related histology, Fumarate hydratase (FH) associated RCC.

  7. Thyroid cancers with Medullary subtype, a cribriform morular subtype of papillary thyroid cancer Papillary/ follicular thyroid cancer, and additional carney complex or Cowden syndrome.

  8. Gastric cancers: Diffuse type, signet ring cell type, and mismatch repair deficient.

  9. Melanoma: Melanoma and pancreatic cancer/ Astrocytoma in the same person.

  10. Lynch syndrome-related cancer (i.e., colorectal, endometrial, gastric, ovarian, pancreatic, ureter and renal pelvic, brain (usually glioblastoma), biliary tract, small intestinal, sebaceous adenoma, sebaceous carcinoma, or keratoacanthoma) and the tumor shows evidence of mismatch repair (MMR) deficiency (either by microsatellite instability or loss of MMR protein expression).[21]

CATEGORIZATION OF GENETIC SYNDROMES BASED ON MOLECULAR PATHWAYS

The syndromes are supposed to be activate pathways that lead to uncontrolled proliferation, increased angiogenesis, or defective repair.

  • Defect in PI3K/AKT/mTOR pathway: This is the most common pathway activated in cancer, which leads to the formation of hamartomas/overgrowth syndromes. Cowden syndrome (PTEN gene), Proteus syndrome (AKT1), Tuberous sclerosis complex (TSC1/2), Von Recklinghausen disease (NF1 and NF2).[22]

  • Defect in the RAS/RAF/MEK/ERK pathway: RASopathies, Von Recklinghausen disease (NF1 and NF2).[23]

  • Defect in angiogenesis: VHL

  • Defects DNA repair mechanism: Hereditary breast and ovarian cancer syndrome, Lynch syndrome, LFS

  • Defect in growth factor regulation: Gorlin syndrome (PTCH1)

  • Others: FAP (APC).

TECHNIQUES

NGS-based methods are used to rapidly sequence known cancer-associated genes for identifying germline mutations at once or identify novel germline variants linked to cancer. NGS platforms allow researchers to sequence millions of DNA fragments in parallel, greatly accelerating the process and reducing the cost per base. The high-throughput data generated through NGS make them particularly valuable for understanding complex diseases, including cancers. Among different cancers, there is significant interest in studying those with a familial predisposition, as they offer opportunities to identify novel genes or gene variants that contribute to cancer development and can be detected at the germline level, thus playing a role in cancer pathogenesis.[24,25]

Multiplex-Ligation Dependent Probe Amplification is another technique that identifies large deletions/duplications in genes. It combines aspects of both polymerase chain reaction and hybridization techniques to analyze the copy number of specific DNA sequences.[26] Sanger sequencing chain termination sequencing technology is a method used to determine the nucleotide sequence of DNA.[27] It can be used for mutation confirmation among at-risk family members, siblings, and next generations. It has its limitations in sequencing a single gene as compared to massively parallel sequencing millions of fragments sequenced in NGS.

REPORTING OF VARIANTS ACCORDING TO AMERICAN COLLEGE OF MEDICAL GENETICS AND GENOMICS (ACMG) GUIDELINES

The ACMG guidelines assist in the evaluation and accurate interpretation of genetic variants and have laid down specific criteria for variant classification and reporting based on their association with genetic and clinical presentation. It outlines a systematic approach to evaluating genetic variants based on multiple lines of evidence, such as population frequency, functional studies, computational, predictive, segregation data, clinical observations, and multiple databases [Table 2]. The guidelines also define standardized terms and criteria for variant interpretation, including pathogenic, likely pathogenic, uncertain significance, likely benign, and benign, based on the available evidence. The collective evidence from all the criteria evaluated, is then used to assign the appropriate variant classification.[28]

Table 2: American college of medical genetics and genomics evidences for classifying gene variants.
Strong Supporting Supporting Moderate Strong Very strong
Population data MAF is too high for disorder BA1/BS1 OR observation in controls inconsistent with disease penetrance BS2 Absent in population databases PM2 Prevalence in affected statistically increased over controls PS4
Computational and predictive data Multiple lines of computational evidence suggest no impact on gene/gene product BP4
Missense in gene where only truncating cause disease BP1
Silent variant with non predicted splice impact BP7
Multiple lines of computational evidence support a deleterious effect on the gene/gene product PP3 Novel missense change at an amino acid residue where a different pathogenic missense change has been seen before PM5
Protein length changing variant PM4
Same amino acid change as an established pathogenic variant PS1 Predicted null variant in a gene where LOF is a known mechanism of disease PVS1
Functional data Well-established functional studies show no deleterious effect BS3 Missense in gene with low rate of benign missense variants and path. Missenses common PP2 Mutational hotspot or well-studied functional domain without benign variation PM1 Well-established functional studies show a deleterious effect PS3
Segregation data Non-segregation with disease BS4 N≤1/8 if 1 family N≤1/4 if >1 family N≤1/16 if 1 family
N≤1/8 if >1 family N≤1/32 if 1 family N≤1/16 if >1 family
De novo data De novo (without paternity and maternity confirmed) PM6 De novo (paternity and maternity confirmed) PS2
Allellic data Observed in trans with a dominant variant BP2
Observed in cis with a pathogenic variant BP2
For recessive disorders, detected in trans with a pathogenic variant PM3
Other database Reputablesource without shared data=benign BP6 Reputablesource=pathogenic PP5
Other data Found in case with an alternate cause BP5 Patient’s phenotype or FH highly specific for gene PP4

GENETIC COUNSELING

Professional societies’ consensus statements recommend pre-test and post-test genetic counseling to patients for Germline hereditary cancer genetic testing.[29] Taking into account the clinical and family history of the patient, informing the patient about indications of genetic testing, as well as addressing the patient’s concerns is an important aspect to consider before ordering a genetic test.[30,31] Constructing a pedigree chart for 3 generations is the best way to visualize risk assessment among patients.[32,33]

The process of obtaining consent for germline testing should comprise an explanation of the test’s rationale, potential outcomes, risks, and advantages. The results of a genetic test should be disclosed followed up with proper post-test genetic counseling.[31]

Post-test genetic counseling involves providing patients and their families with an in-depth summary of the genetic testing results, helping them to comprehend the test outcomes and associated risks; co-ordinating comprehensive follow-up care to plan for cancer prevention, timely surveillance, and offering individuals personalized treatment strategies. If an individual is tested “positive” for a germline variant associated with hereditary cancer, other at-risk family members should be encouraged to follow up for genetic counseling.[34]

MANAGEMENT

Personalized approaches to cancer genetic syndrome

Individuals who test positive for cancer-associated germline mutations require a comprehensive approach to cancer management. Compared to the general population, individuals with cancer germline mutations may necessitate preventive and specialized screening options tailored to the specific associated cancer risks. In certain cases [Table 3], risk reduction surgery options may be recommended to minimize the likelihood of cancer development. For instance, patients consider prophylactic surgeries like mastectomy/oophorectomy/ colectomy to decrease the risk of respective cancer.[35]

Table 3: Personalized therapy approvals by FDA and evidence-based studies in germline hereditary cancers.
Gene Drug Relationship Study method
TSC1/2 (mTOR Pathway) Estrogen-based medications, including oral contraceptives
Everolimus, mTOR inhibitor
Shared risk outcome of progression to lymphangioleiomyomatosis
TSC-associated Subependymal giant Astrocytoma and renal angiomyolipoma
Case Report[36]
Case series[37]
FDA Approved 2018[38]
VHL (Hypoxia induced factor-a) WELIREG, HIF-2a inhibitors Sensitivity
VHL - RCC, CNS Hemangioblastomas, or pancreatic neuroendocrine tumors, not requiring immediate surgery.
FDA approval in 2021[39]
BRCA1/2
(HRR Pathway)
Oral Contraceptives
Clomiphene Citrate
Carboplatin, Cisplatin
PARP inhibitors
Increased risk of breast cancer
(Clinical context)
Complete pathological response
Increased sensitivity
Disease free survival
Retrospective study[40]
Case-control questionnaire[41]
Hahnen et al. 2017[42]
Caramelo et al. 2019[43]
FDA 2018 (Breast), 2020 (Ovary), 2023 (Prostate)[44-46]
MLH1
MSH2
MSH6
PMS2
Aspirin and NSAIDs Sensitivity 10-years follow-up of randomized controlled trial[47]
Phase 1 clinical trial[48]
TP53 Genotoxic agents like etoposide and radiotherapy Resistance Review[49]
In vivomodels[50]
Carboplatin and Breast Cancer Sensitivity Cohort Study[51]
APC
(WNT Pathway)
Aspirin and NSAIDs Shared risk outcome FDA approved in 2018[52]
RET Tyrosine Kinase Inhibitors
Pralsetinib and selpercatinib
Sensitivity
RET mutation-positive medullary thyroid cancer
(MEN syndrome)
FDA approved in 2010[53]
SDHA Tyrosine kinase inhibitors Resistance (GISTs) Observational study[54]
Sensitivity (Metastatic PGG and PCC) Phase 2 clinical trial[55]
SDHB Temozolomide Sensitivity Retrospective population study[56]
Tyrosine Kinase Inhibitors Resistance (GISTs) Observational study[54]
Case report[57]
Sensitivity (Metastatic PGG and PCC) Phase 2 clinical trial[55]
SDHC Tyrosine kinase inhibitors Sensitivity (RCC) Case Report[58]

MEN: Multiple endocrine neoplasia, VHL: Von hippel-lindau, TSC: Tuberous-sclerosis-complex, CNS: Central nervous system, FDA: Food and Drug Administration, NSAIDs: Non-steroidal anti-inflammatory drugs, MEN: Multiple endocrine neoplasia, GIST: Gastrointestinal stromal tumor, RCC: Renal cell carcinoma, PGC: Paraganglioma, PCC: Pheochromocytoma

Genetic risk predictions models

It has become increasingly common to use computational models in genetic risk prediction models in recent years. A number of risk assessment tools and models are available to evaluate the probability that an individual carries a genetic mutation or their risk to develop cancer. These tools assess the risk based on the presence or absence of gene mutations, personal or family history of cancer. BRCAPRO is an important risk model, developed based on the statistical R package, BayesMendel.[59] It calculates the individual’s probability of carrying a pathogenic BRCA1 or BRCA2 gene mutation, the risk of developing contralateral breast cancer and ovarian cancer at different ages. Users can input clinical information such as age, tumor marker information, mastectomy, and oophorectomy information, family race, and ethnicity. BRCAPRO serves as a helpful tool for guiding individuals on whether to pursue genetic testing.[60]

Another breast cancer risk assessment tool is the Breast Cancer Risk Assessment Tool: Gail Model, named after Gail et al., is a statistical model that uses clinical information such as age, menstruation age, age at first live childbirth, and family history of cancer to estimate the risk of developing cancer.[61,62]

While these models may help in understanding the risk of an individual, this alone should not be used as a deciding factor to undergo genetic testing as they have certain limitations. Use of the tools might support decisions for a requirement for genetic testing, but it could also lead to stress and anxiety for patients and their family members if the patient is not guided and explained about the risk-assessment model and its implications.

CONCLUSION

Genetic testing for cancer may help in the estimation of an individual’s lifetime risk of developing cancer by identifying specific genetic changes or mutations. Germline testing is a powerful tool for early detection and cancer prevention not only in proband cases but also in family members. Understanding the indications for germline testing is a responsibility for coordinating this care between the patient and clinician. Choosing the right test with the right technology will help in the correct interpretation of the results and guide the patient and their family in disease prognosis and awareness of preventative screening options, if available.

Acknowledgment

We thank all the clinicians for their participation in this study and all co-workers in the laboratory for their excellent technical assistance.

Declaration of patient consent

Patient’s consent not required as there are no patients in this study.

Conflicts of interest

There are no conflicts of interest.

Use of artificial intelligence (AI)-assisted technology for manuscript preparation

The author(s) confirms that there was no use of Artificial Intelligence (AI)-Assisted Technology for assisting in the writing or editing of the manuscript and no images were manipulated using the AI.

Financial support and sponsorship

Nil.

References

  1. , , , , . Population landscape of familial cancer. Sci Rep. 2015;5:12891.
    [CrossRef] [PubMed] [Google Scholar]
  2. , , , , , . Familial risks and proportions describing population landscape of familial cancer. Cancers (Basel). 2021;13:4385.
    [CrossRef] [PubMed] [Google Scholar]
  3. , , , , . The population impact of familial cancer, a major cause of cancer. Int J Cancer. 2014;134:1899-906.
    [CrossRef] [PubMed] [Google Scholar]
  4. , . Hereditary cancer syndromes. Dtsch Arztebl Int. 2008;105:706-14.
    [CrossRef] [PubMed] [Google Scholar]
  5. , , , , , , et al. Multi-gene panel testing of 23,179 individuals for hereditary cancer risk identifies pathogenic variant carriers missed by current genetic testing guidelines. J Mol Diagn. 2019;21:646-57.
    [CrossRef] [PubMed] [Google Scholar]
  6. , , , , , , et al. Multiple colorectal adenomas, classic adenomatous polyposis, and germ-line mutations in MYH. N Engl J Med. 2003;348:791-9.
    [CrossRef] [PubMed] [Google Scholar]
  7. . Traite des Tumeurs Paris: P. Asselin; .
    [Google Scholar]
  8. . Heredity with reference to carcinoma as shown by the study of the cases examined in the Pathological Laboratory of the University of Michigan, 1895-1912. Arch Int Med. 1913;12:546-55.
    [CrossRef] [Google Scholar]
  9. , , , , . Hereditary factors in cancer. Study of two large midwestern kindreds. Arch Intern Med. 1966;117:206-12.
    [CrossRef] [PubMed] [Google Scholar]
  10. , . Soft-tissue sarcomas, breast cancer, and other neoplasms. A familial syndrome? Ann Intern Med 1969. ;. ;71:747-52.
    [CrossRef] [PubMed] [Google Scholar]
  11. . Mutation and cancer: Statistical study of retinoblastoma. Proc Natl Acad Sci U S A. 1971;68:820-3.
    [CrossRef] [PubMed] [Google Scholar]
  12. , , , , , , et al. A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma. Nature. 1986;323:643-6.
    [CrossRef] [PubMed] [Google Scholar]
  13. . Realizing the promise of cancer predisposition genes. Nature. 2014;505:302-8.
    [CrossRef] [PubMed] [Google Scholar]
  14. , . Hereditary cancer predisposition syndromes. J Clin Oncol. 2005;23:276-92.
    [CrossRef] [PubMed] [Google Scholar]
  15. , , , , , , et al. Somatic genomic testing in patients with metastatic or advanced cancer: ASCO provisional clinical opinion. J Clin Oncol. 2022;40:1231-58.
    [CrossRef] [PubMed] [Google Scholar]
  16. , , , , , , et al. Integrating somatic and germline next-generation sequencing into routine clinical oncology practice. JCO Precis Oncol. 2021;5:884-95.
    [CrossRef] [PubMed] [Google Scholar]
  17. , , , . Aligning germline cancer predisposition with tumor-based next-generation sequencing for modern oncology diagnosis, interception, and therapeutic development. Am Soc Clin Oncol Educ Book. 2023;43:e390738.
    [CrossRef] [PubMed] [Google Scholar]
  18. , . The future of parallel tumor and germline genetic testing: Is there a role for all patients with cancer? J Natl Compr Canc Netw. 2021;19:871-8.
    [CrossRef] [PubMed] [Google Scholar]
  19. , , , , , . Penetrance and expressivity in inherited cancer predisposing syndromes. Trends Cancer. 2018;4:718-28.
    [CrossRef] [PubMed] [Google Scholar]
  20. , . Incomplete penetrance and variable expressivity: From clinical studies to population cohorts. Front Genet. 2022;13:920390.
    [CrossRef] [PubMed] [Google Scholar]
  21. , . Current approaches to germline cancer genetic testing. Annu Rev Med. 2020;71:85-102.
    [CrossRef] [PubMed] [Google Scholar]
  22. , , , , , . Secondary prevention in hereditary breast and/ or ovarian cancer syndromes other than BRCA. J Oncol. 2020;2020:6384190.
    [CrossRef] [PubMed] [Google Scholar]
  23. . Current understanding of neurofibromatosis Type 1, 2, and schwannomatosis. Int J Mol Sci. 2021;22:5850.
    [CrossRef] [PubMed] [Google Scholar]
  24. , , , , . Inherited cancer in the age of next-generation sequencing. Biol Res Nurs. 2018;20:192-204.
    [CrossRef] [PubMed] [Google Scholar]
  25. , , , , , , et al. Applications of next generation sequencing to the analysis of familial breast/ovarian cancer. High Throughput. 2020;9:1.
    [CrossRef] [PubMed] [Google Scholar]
  26. , , , . Use of the MLPA assay in the molecular diagnosis of gene copy number alterations in human genetic diseases. Int J Mol Sci. 2012;13:3245-76.
    [CrossRef] [PubMed] [Google Scholar]
  27. , , . DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A. 1977;74:5463-7.
    [CrossRef] [PubMed] [Google Scholar]
  28. , , , , , , et al. Standards and guidelines for the interpretation of sequence variants: A joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015;17:405-24.
    [CrossRef] [PubMed] [Google Scholar]
  29. , , , , , , et al. Patients' experiences with pre-test genetic counseling provided by breast cancer healthcare professionals: Results from a large prospective multicenter study. Breast. 2023;69:349-57.
    [CrossRef] [PubMed] [Google Scholar]
  30. , , , , . Assessing Genetic Risks: Implications for Health and Social Policy Washington, DC: National Academies Press (US); .
    [Google Scholar]
  31. , , , . Genetic counselors: Translating genomic science into clinical practice. J Clin Invest. 2003;112:1274-9.
    [CrossRef] [PubMed] [Google Scholar]
  32. . The three-generation pedigree: A critical tool in cancer genetics care. Oncol Nurs Forum. 2016;43:655-60.
    [CrossRef] [PubMed] [Google Scholar]
  33. , , , , , , et al. Recommendations for standardized human pedigree nomenclature. Pedigree Standardization Task Force of the National Society of Genetic Counselors. Am J Hum Genet. 1995;56:745-52.
    [Google Scholar]
  34. . Points to consider: Ethical, legal, and psychosocial implications of genetic testing in children and adolescents. American Society of Human Genetics Board of Directors. American College of Medical Genetics Board of Directors. Am J Hum Genet. 1995;57:1233-41.
    [Google Scholar]
  35. , , . Pharmacogenetic review: Germline genetic variants possessing increased cancer risk with clinically actionable therapeutic relationships. Front Genet. 2022;13:857120.
    [CrossRef] [PubMed] [Google Scholar]
  36. . Exacerbation of pulmonary lymphangioleiomyomatosis by exogenous oestrogen used for infertility treatment. Thorax. 2002;57:1085-6.
    [CrossRef] [PubMed] [Google Scholar]
  37. , , , . Pulmonary lymphangioleiomyomatosis (LAM): Examining oral contraceptive pills and the onset of disease. J Womens Health (Larchmt). 2003;12:81-5.
    [CrossRef] [PubMed] [Google Scholar]
  38. , , , , . Use of oral contraceptives in BRCA mutation carriers and risk for ovarian and breast cancer: A systematic review. Arch Gynecol Obstet. 2020;301:875-84.
    [CrossRef] [PubMed] [Google Scholar]
  39. , , , , , , et al. Cancer risk in women treated with fertility drugs according to parity status-a registry-based cohort study. Cancer Epidemiol Biomarkers Prev. 2017;26:953-62.
    [CrossRef] [PubMed] [Google Scholar]
  40. , , , , , , et al. Germline mutation status, pathological complete response, and disease-free survival in triple-negative breast cancer: Secondary Analysis of the GeparSixto Randomized Clinical Trial. JAMA Oncol. 2017;3:1378-85.
    [CrossRef] [PubMed] [Google Scholar]
  41. , , , , . The effect of neoadjuvant platinum-based chemotherapy in BRCA mutated triple negative breast cancers-systematic review and meta-analysis. Hered Cancer Clin Pract. 2019;17:11.
    [CrossRef] [PubMed] [Google Scholar]
  42. , , , , , , et al. Cancer prevention with aspirin in hereditary colorectal cancer (lynch syndrome), 10-year follow-up and registry-based 20-year data in the CAPP2 study: A double-blind, randomised, placebo-controlled trial. Lancet. 2020;395:1855-63.
    [CrossRef] [PubMed] [Google Scholar]
  43. , , , , , , et al. Naproxen chemoprevention promotes immune activation in Lynch syndrome colorectal mucosa. Gut. 2021;70:555-66.
    [CrossRef] [PubMed] [Google Scholar]
  44. , , , , , . Guidelines for the Li-Fraumeni and heritable TP53-related cancer syndromes. Eur J Hum Genet. 2020;28:1379-86.
    [CrossRef] [PubMed] [Google Scholar]
  45. , , , , , , et al. Contribution of genotoxic anticancer treatments to the development of multiple primary tumours in the context of germline TP53 mutations. Eur J Cancer. 2018;101:254-62.
    [CrossRef] [PubMed] [Google Scholar]
  46. , , , , , , et al. Prevalence and clinical impact of TP53 germline mutations in Chinese women with breast cancer. Int J Cancer. 2020;146:487-95.
    [CrossRef] [PubMed] [Google Scholar]
  47. . CELEBREX® (Celecoxib) In: Silver Spring, MD: U.S Food and Drug Administration. .
    [Google Scholar]
  48. . VICTOZA® (Liraglutide) Injection, for Subcutaneous Use In: Silver Spring, MD: U.S Food and Drug Administration. .
    [Google Scholar]
  49. , , , , , , et al. Molecular subtypes of KIT/PDGFRA wild-type gastrointestinal stromal tumors: A report from the national institutes of health gastrointestinal stromal tumor clinic. JAMA Oncol. 2016;2:922-8.
    [CrossRef] [PubMed] [Google Scholar]
  50. , , , , , , et al. A phase 2 trial of sunitinib in patients with progressive paraganglioma or pheochromocytoma: The SNIPP trial. Br J Cancer. 2019;120:1113-9.
    [CrossRef] [PubMed] [Google Scholar]
  51. , , , , , , et al. SDHB mutations are associated with response to temozolomide in patients with metastatic pheochromocytoma or paraganglioma. Int J Cancer. 2014;135:2711-20.
    [CrossRef] [PubMed] [Google Scholar]
  52. , , , , , , et al. Renal carcinoma associated with succinate dehydrogenase B mutation: A new and unique subtype of renal carcinoma. J Clin Oncol. 2014;32:e10-3.
    [CrossRef] [PubMed] [Google Scholar]
  53. , , , , , , et al. Vascular endothelial growth factor receptor-targeted therapy in succinate dehydrogenase C kidney cancer. J Clin Oncol. 2016;34:e76-9.
    [CrossRef] [PubMed] [Google Scholar]
  54. , , , , . BayesMendel: An R environment for Mendelian risk prediction. Stat Appl Genet Mol Biol. 2004;3:21.
    [CrossRef] [PubMed] [Google Scholar]
  55. , , , . Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO. Cancer Inform. 2015;14:147-57.
    [CrossRef] [PubMed] [Google Scholar]
  56. , , , , , , et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81:1879-86.
    [CrossRef] [PubMed] [Google Scholar]
  57. , , , , , , et al. Use and applicability of the gail model to calculate breast cancer risk: A scoping review. Asian Pac J Cancer Prev. 2022;23:1117-23.
    [CrossRef] [PubMed] [Google Scholar]
Show Sections