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Lecture notes Tumor biology and clinical behavior
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Lectures Tumor Biology and ClinicalBehaviour
Exam 60%
Presentation 30%
Active participation in journal clubs, lectures and research proposal 10%
Research proposal: Diagnostic biomarkers for pancreatic cancer
- Background: what is the problem?
- Aim: cure cancer
- Plan of investigation: what experiments will be done to address the problem?
- Expected results & data analysis: what will the data tell you? Address the problem!
HC 1 Intro to histopathology wand staging of tumors
Neoplasia = new growth. A neoplasm is an abnormal mass of tissue, the growth of which exceeds
and is uncoordinated with that of the normal tissue and persists in the same excessive manner after
cessation of the stimuli which evoked the change.
Tumor is not the same as neoplasia! →New growth, can be malignant of benign and spontaneous
involution.
- Neoplasia from the lung: architecture is still there, but there is something growing in the
cavities with macrophages or other immune cells > so it looks like a tumor, but it is neoplasia
because the normal structure is still there.
Nomenclature: neoplastic cells (cell of origin) and reactive stroma: mesenchymal, epithelial or mixed
(more than one neoplastic cell line) origin. Benign tumor end on -oma and the malignant ones on
carcinoma/sarcoma (malignant tumor of the mesenchymal cells). But the haematological cells are an
exception. Analysis of pathologist:
- Architecture
o Anything like pre-existent organ?
o Destruction of normal tissue?
- Cellular details
o Cell size (says nothing about if it is benign or malignant)
o Nuclear aspect
o Nucleus/cytoplasm ratio
➔ Benign tumors are well differentiated,
they grow slowly (might become big),
usually cohesive (push normal tissue,
but no infiltration), an no metastasis.
Invasive growth is not the same as
metastasis.
➔ Malignant tumors are poorly
differentiated (anaplasia) with an
atypical structure, grow fast (growth
rate is inverse to differentiation), locally
invasive with frequent metastasis.
,3 main metastatic pathways:
- Lymphogenic
- Hematogenic (less predictive), clear lining of the capillary
- Via body cavities: pleural and peritoneal
o Pseudomyxoma peritonei > outgrowth, within the peritoneal cavity, of neoplastic
mucus-producing epithelium (almost always derived from the appendix).
Tumor staging = TNM staging: indicate how extensive the situation is
- T = tumor size and local spread of the primary tumor (staging of the tumor)
- N = nodes, metastasis to regional lymph nodes (clinical autopsy)
- M = metastasis/distant, metastasis to distant sites.
The epidemiology can give some clues about the biology by examining the interactions between the
environment and genetic factors. Genetic factors say something about predispositions.
Tumor type and grade are not the same:
- Type is based on types of differentiation of tumor cells (cell lineage).
- Grade is based on various features:
o Cytological atypia
o Mitosis activity
o Necrosis
o Degree of resemblance to normal tissue counterpart
Clinicians will treat the patients with many varying therapies. So the aim of the histopathology is to
guide the clinicians. Prognostic biomarkers are used to identify the likelihood of a clinical event,
disease recurrence or progression. Predictive biomarkers are used to identify individuals who are
more likely than similar individuals without the biomarker to experience a (un)favourable effect of a
treatment. A pathologic and genetic classification of human tumors designed to be accepted and
used worldwide. It provides a standard criteria for pathology, clinical practice, cancer registration,
epidemiologic studies, clinical trials, and cancer research.
Take home:
- Not every tumor is a neoplasm.
- Pathologist assists the diagnostics and grading of the tumor.
- Diagnosis, grade and stage are prognostic and predictive markers.
HC 2 cancer epidemiology
Epidemiology descends from the Greek words epi, demos and logos. Epi means on, demos means
people and logos means reason. Definition cancer epidemiology is to study the distribution of cancer
in the population. The goal is identify risk factors in the population that may lead to the introduction
of preventive measures.
History:
- Percival Pott (1775): chirurgical observations > scrotal cancer was common among chimney
sweeps. Pott suggested that scrotal cancer is associated with exposure to scoot.
- Henry Butlin (1845-1912) > observed that the absence of the disease was due to protective
clothing. In 1875 a successful legislation was established and supported by police
enforcement.
, - Hoffman (1915): the mortality if cancer throughout the world > one of the earliest reports in
international cancer mortality statistics. The power of data: oral cancer occurred almost
exclusively in men > Hoffman suggested that oral cancer may be related to smoking.
- Doll and Hill (1954): the mortality of doctors in relation to their smoking habits >
questionnaires about smoking were filled out and death from lung cancer and other cancer
were registered.
In epidemiology they work with the total number is replaced by person years > people may enter the
population at baseline or later or drop out early or die. When you have measured the person years
you can define the crude rate per 100,000 person years:
# 𝑛𝑒𝑤 𝑐𝑎𝑠𝑒𝑠 𝑖𝑛 𝑎 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑝𝑒𝑟𝑖𝑜𝑑
𝑥 100,000
𝑡𝑜𝑡𝑎𝑙 # 𝑜𝑓 𝑝𝑒𝑟𝑠𝑜𝑛 − 𝑦𝑒𝑎𝑟𝑠 𝑎𝑟 𝑟𝑖𝑠𝑘 𝑑𝑢𝑟𝑖𝑛𝑔 𝑡ℎ𝑎𝑡 𝑝𝑒𝑟𝑖𝑜𝑑
𝑟𝑎𝑡𝑒 𝑜𝑓 𝑑𝑒𝑎𝑡ℎ 𝑓𝑟𝑜𝑚 𝑐𝑎𝑛𝑐𝑒𝑟 𝑎𝑚𝑜𝑛𝑔 𝑠𝑚𝑜𝑘𝑒𝑟𝑠
Rate ratio: 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑑𝑒𝑎𝑡ℎ 𝑎𝑚𝑜𝑛𝑔 𝑛𝑜𝑛−𝑠𝑚𝑜𝑘𝑒𝑟𝑠
- Rate of death from cancer = mortality
- Rate of new cancer cases = incidence
o Increases with age
Age-specific-rate per 100,000 person years:
# 𝑜𝑓 𝑛𝑒𝑤 𝑐𝑎𝑠𝑒𝑠 𝑎𝑟𝑖𝑠𝑖𝑛𝑔 𝑖𝑛 𝑎 𝑐𝑒𝑟𝑡𝑎𝑖𝑛 𝑎𝑔𝑒 𝑔𝑟𝑜𝑢𝑝 𝑖𝑛 𝑎 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑝𝑒𝑟𝑖𝑜𝑑
𝑥 100,000
𝑡𝑜𝑡𝑎𝑙 # 𝑜𝑓 𝑝𝑒𝑟𝑠𝑜𝑛 − 𝑦𝑒𝑎𝑟𝑠 𝑎𝑡 𝑟𝑖𝑠𝑘 𝑖𝑛 𝑡ℎ𝑎𝑡 𝑎𝑔𝑒 𝑔𝑟𝑜𝑢𝑝 𝑑𝑢𝑟𝑖𝑛𝑔 𝑡ℎ𝑎𝑡 𝑝𝑒𝑟𝑖𝑜𝑑 𝑜𝑓 𝑡𝑖𝑚𝑒
If you compare populations you need to adjust crude rates by projecting age-specific rates onto a
population with a specific age distribution e.g. 0-44y = 74%, 45-64y = 19%, 65+ = 7%. → standardized
rates of stomach cancer cases among males.
Widely used standard population are the European and World population. If sample sizes are large,
use 5-years age strata. The age-standardized rates based on these populations are:
- European (ESR)
- World (WSR)
Take home 1: when comparing two different populations, do not use crude rates but only age-
standardized rates.
Identifying risk factors is difficult because most of the collected data is observational/non-
experimental and is difficult to assess because time from start exposure of risk factor until clinical
manifestation of disease is long AND occurrence of cancer disease in the general population is often
low.
Studies can be ranked according to level of empirical evidence:
1. RCT > holy grail but expensive: compare intervention with control
group. Group allocation by random number generator.
a. Cameron an Pauling (PNAS, 1976) compared an
intervention group with a historical control group and
found a four times longer survival time among patients who
received vitamin C.
b. Potential problems:
, i. Patients in the intervention group were still alive when they were judged as
untreatable. Thereof, the judgement about who is terminally ill may be
imprecise.
ii. The date of untreatability in the control group was retrieved from records. In
the intervention group, the date of untreatability was the date of start
treatment.
c. Moertel (1985) did a RCT and did not find an effect in vitamin C.
d. But an RCT is not always possible because of ethical and/or technical reasons.
2. Cohort > group allocation not determined by investigator. A cohort
study may suffer from several types of bias which should be
carefully examined. Confounding of exposure effects by other
variables (e.g. age and sex). Besides that, it is very expensive and
can take many years before you get a result.
a. Prospective study (follow over time)
3. Case-control > match each cancer patient to one or several control
patients without cancer, this means that cancer and non-cancer
patients are similar with regard to risk factors other than the
exposure risk factor under study, each case was matched to four
controls.
a. Retrospective study (go back in time)
b. Example: association between DES taken during pregnancy
and vaginal cancer in daughters > 7/8 patients with cancer had prenatal exposure to
DES, 0 patients without cancer had exposure to DES > define Odds ratio:
# 𝑒𝑥𝑝𝑜𝑠𝑒𝑑 𝑐𝑎𝑛𝑐𝑒𝑟 𝑝𝑎𝑡𝑖𝑒𝑛𝑡𝑠
⁄# 𝑛𝑜𝑛−𝑒𝑥𝑝𝑜𝑠𝑒𝑑 𝑐𝑎𝑛𝑐𝑒𝑟 𝑝𝑎𝑡𝑖𝑒𝑛𝑡𝑠
i. # 𝑒𝑥𝑝𝑜𝑠𝑒𝑑 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠
⁄# 𝑛𝑜𝑛−𝑒𝑥𝑝𝑜𝑠𝑒𝑑 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠
c. Fisher exact p-value < 0.001
d. Conclusion: small study and time from exposure to manifestation of disease is very
long. Nevertheless, statistical evidence is strong.
e. Concerns:
i. Case-control studies are a precise task; patients in the cancer group must be
matched to patients in the control group. Each patients in the cancer group
should be matched to a control patient with the same age, sex, life style.
ii. Selection bias: cancer patients must be representative of the population of
interest.
iii. Recall bias: cancer cases and controls must recall exposure accurately.
Cancer patients often have a better recall because they are active with their
disease.
4. Routine-data study, two types
a. Individual level data where exposure information of each individual is linked to
disease status. Link between risk factor and disease for each patients
b. Aggregated level data where it is not possible to link the exposure of the individual to
his outcome. The exposure information is defined on the group level. Link between
risk factor and disease on a aggregative-data level > association study
i. Example nuclear weapon testing and childhood leukemia. Higher incidence
when weapons were tested above the ground. However, other cancer
incidences decreased > so you need to have a clear context. Here the
associations found in registry data pointed at different directions. This has
stimulated further research.
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